Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time.

The evidence to be evaluated is:We similarly use axis-aligned 2D grids of features for self-supervised scene representation via neural rendering, but reconstruct them directly from few or a single 2D image observations.

Some prior work leverages object motion across video frames to learn separate representations for movable foreground and static background in 2D (Kasten et al., 2021; Ye et al., 2022; Bao et al., 2022), while other recent work can also learn 3D representations (Yuan et al., 2021; Tschernezki et al., 2021). Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

The method will be trained on a dataset of multi-view videos with calibrated cameras with wide baselines.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

We now describe training on multi-view video to learn to disentangle static and dynamic components of the scene.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features. The per-frame static groundplans are mean-pooled to obtain a single, time-independent static groundplan.

We train our model on multi-view video, where multi-view information is used to learn the 3D structure, while motion is used to disentangle the static and dynamic components in the scene.

For each time-step, we sample multiple images from different camera views; some of the views are used as input to the method while others are used to compute the loss function. We use the input images to infer static and dynamic groundplans, and use them to render out per-frame query views.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively.

Our method is trained on multi-view observations of dynamic scenes.

CoSY enables generation of a high-quality, path-traced dataset of multi-view videos with large camera baselines. We rendered multi-view observations of \(9000\) scenes with moving cars, sampled using \(15\) background city models and \(95\) car models. We train on \(8000\) scenes, and evenly split the rest into validation and test sets.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene.

As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements. By leveraging multi-view video at training time, we can reconstruct disentangled 3D representations from a single image observation at test time.

* Yariv et al. (2020) Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. _Proc. NeurIPS_, 2020.

* Yuan et al. (2021) Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, and Steven Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In _Proc. CVPR_, pp. 13144-13152, 2021.

**Multi-view input**. In case of multi-view inputs, we average the unprojected latents for all 3D points in the canonical 3D coordinate system for all views. This results, like in the monocular case, in a 3D volume of \(128\times 64\times 16\times 64\) (\(CH\times X\times Y\times Z\)).

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our model supports multi-view, as well as monocular reconstruction. We start our training only using multi-view reconstruction with 5 input views for the first 200 epochs, and then switch to a variable mode where the model is given a varying number of input views ranging between 1-5 views. Thiscurriculum approach helps the network to first learn the relevant scene priors, before learning to complete the 3D structure of the scene.

Our method successfully integrates information across observations into a single, multi-view consistent representation. The background reconstructions improve with more input views, as a result of less uncertainty for the occluded regions.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles.

The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods. To demonstrate the efficacy of the groundplans for frame interpolation, we trained RIFE Huang et al. (2020) on our model trained on the GQN-rooms Eslami et al. (2018) dataset. We generated simple linear motion trajectories for objects over 10 frames.

The scene was rendered from 15 different camera views. We first trained our model on the GQN dataset and used the output dynamic groundplans to train the frame interpolation method.

Firstly, we extracted dynamic groundplan for the samples over different timesteps by passing the multi-view observation through our groundplan generation pipeline.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 5: Single-image reconstruction, disentanglement of static and dynamic objects, and novel view synthesis. Given a single input image, our method can disentangle the observed scene into static and object components based on what the model observed as not-moving and moving in the training data. In these examples, the cars are isolated in the object component as the model was training on video data of cars moving on the road.

Input: Figure 2: Groundplan inference. Given a context image, we first extract a set of CNN features. We unproject the features into 3D and re-sample them at “pillars” on top of the location of groundplan vertices. Pillars are aggregated into groundplan features using a softmax-weighted sum. The resulting 2D grid of features is decomposed into separate dynamic and static groundplans by a 2D CNN. The coordinate-encoding MLP is not visualized in this figure. Please refer to Sec. 3 for details.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 9: Visual quality of views for a given input image.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 3: Learning Static-Dynamic Disentanglement. Given multiple frames of a video, we extract per-frame, compactified, static and dynamic groundplans according to Fig. 2. Static groundplans are pooled into a time-invariant groundplan. We then composite per-frame dynamic and static time-invariant groundplans via differentiable volume rendering. Our model is supervised only via a re-rendering loss on video frames. We encourage the model to explain as much of the scene density as possible with the static groundplan via a sparsity loss on per-frame dynamic volume rendering densities. The surface loss is not visualized here.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review of the claim and evidence:

The claim that "we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time" appears to be well-stated and supported by the evidence. The paper demonstrates:
- A clear training methodology using multi-view videos
- Successful disentanglement of static/dynamic components shown in multiple figures
- Single-image reconstruction capabilities evidenced in Figure 5
- Comprehensive experimental validation using 9000 scenes
- Practical applications like object editing and motion interpolation

The evidence thoroughly supports each aspect of the claim without exaggeration. The results demonstrate both the multi-view training and single-image reconstruction capabilities as stated.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time.

The review comment to be evaluated is:

Strength:

1. The authors tackle a challenging yet valuable problem, disentanglement of the movable and immovable components of the scene from a single image. They propose a neural scene representation called conditional neural groundplans and a self-supervised framework to disentangle static background and movable foreground objects given only a single image. The framework enables a variety of downstream tasks, such as extraction of object-centric 3D representations, novel view synthesis, instance-level segmentation, 3D bounding box prediction, and scene editing. The direction would interest a wide range of communities, including 3D scene understanding, 3D neural representation, disentangled representation learning, and novel view synthesis.
2. The proposed 3D persistent scene representation learning framework elegantly leverages ideas from BEV representation, neural representation learning, volume rendering, and compositing operation (Yuan et al., 2021). Moreover, the proposed method utilizes multi-view videos (in a self-supervised manner) at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time. The authors present a self-supervised neural scene representation framework for general scene understanding tasks. The reviewer found the work valuable to the community.
3. The proposed 3D scene representations enable novel view synthesis and disentangle movable and immovable components of the scene via diverse qualitative evaluation on two datasets, CLEVR and CoSY. In particular, the qualitative results shown on CoSY are impressive, as shown in the manuscript and supplementary website.

Weaknesses:

1. Persistent scene representation: By sufficient qualitative analysis, the proposed 3D scene representations enable novel view synthesis and disentangle movable and immovable elements for different scene understanding tasks. One failure case shown on the top row of p.17 is that the framework cannot effectively synthesize the viewing perspective behind the box with a green door. The framework seems unable to capture the background correctly, yielding incorrect synthesis. Moreover, the box's location seems incorrect from the images on the left. The reviewer wonder if the performance can be improved with additional observations.
2. The authors propose to have two separate groundplans neural representations for static and dynamic, respectively. As we know from the literature (e.g., Yuan et al., 2021), it is possible to keep separate 3D neural representations for novel view synthesis. It seems reasonable to tackle the goal of this paper, i.e., achieve decent performance on a variety of tasks from a single image, with separate 3D neural representations based on the proposed framework. It would be great for the authors to elaborate on the technical challenge of this direction. That could motivate the community to tackle the direction collectively.
3. Differentiable rendering based on neural groundplans feature: the reviewer found it less intuitive to understand why the differentiable rendering would work using the groundplan features g_x. Specifically, g_x is obtained based on the weighted sum of the features along the y-axis. The groundplan feature g_x contains information in a particular pillar. Why is it a better feature for volume rendering? Empirically, the design choice renders better quality than STOA methods (i.e., PixelNeRF and uORF). Could the authors please elaborate more on this design choice?
4. The design choice of BEV representation: The authors utilize a particular operation to form BEV representations, i.e., the weighted sum of the features. However, there are other approaches, e.g., Roddick and Cipolla, 2020, Philion and Fidler, 2020 and Saha et al., 2022. It would be valuable for the audience to learn the difference between these operations for representation learning.
5. The reviewer is interested in the qualitative performance of novel view synthesis without using the contracted coordinate. With the analysis, readers would be able to visualize the difference.

The evidence to be evaluated is:We similarly use axis-aligned 2D grids of features for self-supervised scene representation via neural rendering, but reconstruct them directly from few or a single 2D image observations.

Some prior work leverages object motion across video frames to learn separate representations for movable foreground and static background in 2D (Kasten et al., 2021; Ye et al., 2022; Bao et al., 2022), while other recent work can also learn 3D representations (Yuan et al., 2021; Tschernezki et al., 2021). Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

The method will be trained on a dataset of multi-view videos with calibrated cameras with wide baselines.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

We now describe training on multi-view video to learn to disentangle static and dynamic components of the scene.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features. The per-frame static groundplans are mean-pooled to obtain a single, time-independent static groundplan.

We train our model on multi-view video, where multi-view information is used to learn the 3D structure, while motion is used to disentangle the static and dynamic components in the scene.

For each time-step, we sample multiple images from different camera views; some of the views are used as input to the method while others are used to compute the loss function. We use the input images to infer static and dynamic groundplans, and use them to render out per-frame query views.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively.

Our method is trained on multi-view observations of dynamic scenes.

CoSY enables generation of a high-quality, path-traced dataset of multi-view videos with large camera baselines. We rendered multi-view observations of \(9000\) scenes with moving cars, sampled using \(15\) background city models and \(95\) car models. We train on \(8000\) scenes, and evenly split the rest into validation and test sets.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene.

As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements. By leveraging multi-view video at training time, we can reconstruct disentangled 3D representations from a single image observation at test time.

* Yariv et al. (2020) Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. _Proc. NeurIPS_, 2020.

* Yuan et al. (2021) Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, and Steven Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In _Proc. CVPR_, pp. 13144-13152, 2021.

**Multi-view input**. In case of multi-view inputs, we average the unprojected latents for all 3D points in the canonical 3D coordinate system for all views. This results, like in the monocular case, in a 3D volume of \(128\times 64\times 16\times 64\) (\(CH\times X\times Y\times Z\)).

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our model supports multi-view, as well as monocular reconstruction. We start our training only using multi-view reconstruction with 5 input views for the first 200 epochs, and then switch to a variable mode where the model is given a varying number of input views ranging between 1-5 views. Thiscurriculum approach helps the network to first learn the relevant scene priors, before learning to complete the 3D structure of the scene.

Our method successfully integrates information across observations into a single, multi-view consistent representation. The background reconstructions improve with more input views, as a result of less uncertainty for the occluded regions.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles.

The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods. To demonstrate the efficacy of the groundplans for frame interpolation, we trained RIFE Huang et al. (2020) on our model trained on the GQN-rooms Eslami et al. (2018) dataset. We generated simple linear motion trajectories for objects over 10 frames.

The scene was rendered from 15 different camera views. We first trained our model on the GQN dataset and used the output dynamic groundplans to train the frame interpolation method.

Firstly, we extracted dynamic groundplan for the samples over different timesteps by passing the multi-view observation through our groundplan generation pipeline.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 5: Single-image reconstruction, disentanglement of static and dynamic objects, and novel view synthesis. Given a single input image, our method can disentangle the observed scene into static and object components based on what the model observed as not-moving and moving in the training data. In these examples, the cars are isolated in the object component as the model was training on video data of cars moving on the road.

Input: Figure 2: Groundplan inference. Given a context image, we first extract a set of CNN features. We unproject the features into 3D and re-sample them at “pillars” on top of the location of groundplan vertices. Pillars are aggregated into groundplan features using a softmax-weighted sum. The resulting 2D grid of features is decomposed into separate dynamic and static groundplans by a 2D CNN. The coordinate-encoding MLP is not visualized in this figure. Please refer to Sec. 3 for details.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 9: Visual quality of views for a given input image.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 3: Learning Static-Dynamic Disentanglement. Given multiple frames of a video, we extract per-frame, compactified, static and dynamic groundplans according to Fig. 2. Static groundplans are pooled into a time-invariant groundplan. We then composite per-frame dynamic and static time-invariant groundplans via differentiable volume rendering. Our model is supervised only via a re-rendering loss on video frames. We encourage the model to explain as much of the scene density as possible with the static groundplan via a sparsity loss on per-frame dynamic volume rendering densities. The surface loss is not visualized here.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, this claim appears to be well-stated (1) because:

The evidence thoroughly demonstrates that the method uses multi-view videos during training to learn separate static and dynamic scene components, which can then be reconstructed from a single image at test time. This is supported by:

- Clear description of the training process using multi-view video data
- Extensive experimental results showing successful disentanglement from single images
- Multiple figures demonstrating static/dynamic separation in novel views
- Qualitative results on both synthetic (CLEVR) and realistic (CoSY) datasets
- Detailed technical approach explaining how the model leverages motion cues during training for disentanglement

The claim accurately represents what the evidence shows, without exaggeration of capabilities or results.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time.

The review comment to be evaluated is:

====> Strength
1. The authors introduced the bird’s eye-view (BEV) representation as a scene representation, which is novel in the area of neural rendering.
2. The disentanglement of the static and moving component of the scene adds more contribution to the proposed method. Although it is not new to automatically separate these components in the neural rendering community, using a vanilla 2D CNN to separate the feature representation is new to the reviewer.
3. The authors show that using a simple heuristic method, they can generate instance segmentation and bounding boxes for the dynamic components in the scene.
4. Good qualitative results are given.

====> Weakness
1. The details of the neural groundplans is confusing:

    i. In page 4, there are multiple $x$ such as $x'$, $x$, $x_c$, $x_i$. Their shape should be clarified. Whether they belong to 2D or 3D point, and also their coordinate system (i.e., the image frame coordinate or the world coordinate, and how to transform between these coordinates using the camera intrinsic)

    ii. For equation like $v(x ′ ) = F(π(C^{-1}(x ′ )))$, $f(x ′ ) = ...$, the parentheses here denote indexing the feature volume $v$ and feature tensor $F$. However, it is easy to confuse them with the functions like $\pi()$ and $D()$. The authors should consider using other formulation.

    iii.The $\pi(\cdot)$ is not clear. What is the meaning of it and how to learn $\pi(\cdot)$, is it learned per image I?

    iv. ``$g_x$ are the groundplan features for the point $x$ computed by projecting the coordinates onto the groundplane...'' How is the projection conducted? Is it projected by the camera extrinsic？

    v. In page 5, the authors say ``The per-frame static groundplans are pooled to obtain a single, time-independent static groundplan.'' How is the pooling process performed? The authors need to detail it.

2. The task setting is not clear. The authors should consider formulate the problem setting in a separated subsection (e.g., train one model for multiple scenes, all images are calibrated, the view density per scene, etc.)

3. It seems the compared baselines like pixelNerf and uORF are not proposed for the topic of separating dynamic and static representation. It would be fairer to compare with ``Neuraldiff: Segmenting 3d objects that move in egocentric videos.'' that used different components for the moving and static objects.

The evidence to be evaluated is:We similarly use axis-aligned 2D grids of features for self-supervised scene representation via neural rendering, but reconstruct them directly from few or a single 2D image observations.

Some prior work leverages object motion across video frames to learn separate representations for movable foreground and static background in 2D (Kasten et al., 2021; Ye et al., 2022; Bao et al., 2022), while other recent work can also learn 3D representations (Yuan et al., 2021; Tschernezki et al., 2021). Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

The method will be trained on a dataset of multi-view videos with calibrated cameras with wide baselines.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

We now describe training on multi-view video to learn to disentangle static and dynamic components of the scene.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features. The per-frame static groundplans are mean-pooled to obtain a single, time-independent static groundplan.

We train our model on multi-view video, where multi-view information is used to learn the 3D structure, while motion is used to disentangle the static and dynamic components in the scene.

For each time-step, we sample multiple images from different camera views; some of the views are used as input to the method while others are used to compute the loss function. We use the input images to infer static and dynamic groundplans, and use them to render out per-frame query views.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively.

Our method is trained on multi-view observations of dynamic scenes.

CoSY enables generation of a high-quality, path-traced dataset of multi-view videos with large camera baselines. We rendered multi-view observations of \(9000\) scenes with moving cars, sampled using \(15\) background city models and \(95\) car models. We train on \(8000\) scenes, and evenly split the rest into validation and test sets.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene.

As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements. By leveraging multi-view video at training time, we can reconstruct disentangled 3D representations from a single image observation at test time.

* Yariv et al. (2020) Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. _Proc. NeurIPS_, 2020.

* Yuan et al. (2021) Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, and Steven Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In _Proc. CVPR_, pp. 13144-13152, 2021.

**Multi-view input**. In case of multi-view inputs, we average the unprojected latents for all 3D points in the canonical 3D coordinate system for all views. This results, like in the monocular case, in a 3D volume of \(128\times 64\times 16\times 64\) (\(CH\times X\times Y\times Z\)).

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our model supports multi-view, as well as monocular reconstruction. We start our training only using multi-view reconstruction with 5 input views for the first 200 epochs, and then switch to a variable mode where the model is given a varying number of input views ranging between 1-5 views. Thiscurriculum approach helps the network to first learn the relevant scene priors, before learning to complete the 3D structure of the scene.

Our method successfully integrates information across observations into a single, multi-view consistent representation. The background reconstructions improve with more input views, as a result of less uncertainty for the occluded regions.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles.

The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods. To demonstrate the efficacy of the groundplans for frame interpolation, we trained RIFE Huang et al. (2020) on our model trained on the GQN-rooms Eslami et al. (2018) dataset. We generated simple linear motion trajectories for objects over 10 frames.

The scene was rendered from 15 different camera views. We first trained our model on the GQN dataset and used the output dynamic groundplans to train the frame interpolation method.

Firstly, we extracted dynamic groundplan for the samples over different timesteps by passing the multi-view observation through our groundplan generation pipeline.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 5: Single-image reconstruction, disentanglement of static and dynamic objects, and novel view synthesis. Given a single input image, our method can disentangle the observed scene into static and object components based on what the model observed as not-moving and moving in the training data. In these examples, the cars are isolated in the object component as the model was training on video data of cars moving on the road.

Input: Figure 2: Groundplan inference. Given a context image, we first extract a set of CNN features. We unproject the features into 3D and re-sample them at “pillars” on top of the location of groundplan vertices. Pillars are aggregated into groundplan features using a softmax-weighted sum. The resulting 2D grid of features is decomposed into separate dynamic and static groundplans by a 2D CNN. The coordinate-encoding MLP is not visualized in this figure. Please refer to Sec. 3 for details.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 9: Visual quality of views for a given input image.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 3: Learning Static-Dynamic Disentanglement. Given multiple frames of a video, we extract per-frame, compactified, static and dynamic groundplans according to Fig. 2. Static groundplans are pooled into a time-invariant groundplan. We then composite per-frame dynamic and static time-invariant groundplans via differentiable volume rendering. Our model is supervised only via a re-rendering loss on video frames. We encourage the model to explain as much of the scene density as possible with the static groundplan via a sparsity loss on per-frame dynamic volume rendering densities. The surface loss is not visualized here.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time" appears to be well-stated and accurately reflects the demonstrated capabilities. The evidence shows:

1. Clear training methodology using multi-view videos to learn disentanglement
2. Demonstrated ability to reconstruct separate static/dynamic components from single images
3. Extensive visual results showing successful disentanglement and reconstruction
4. Technical details of the training process and architecture
5. Validation across multiple scenes and viewpoints

The review comments do not dispute this core claim but rather focus on technical clarifications. The claim avoids overstatement by precisely describing what the method accomplishes without exaggerating its capabilities.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time.

The review comment to be evaluated is:

Strengths include:
* The self-supervised training removes the need for GT annotation, e.g., object bounding boxes, for the supported downstream tasks.
* The decomposition of static and movable object feature spaces looks like quite an elegant solution to utilize the inductive bias in multi-view videos while enabling capabilities such as segmentation and object-level scene editing.
* The results overall look promising. Single-view/few-view novel view synthesis is a very challenging task, and the comparisons with PixelNeRF and uORF clearly demonstrated the advantages in synthesis quality. The analysis of adding more views (Fig.10) is strong evidence that the learned 3D representation can figure out how 2D views are related to, and hence help improve, 3D reconstruction.

Weaknesses include:
* There is no study of the generalization capability of the model. It seems likely that the learned models can overfit the limited appearances in the dataset (a few types of shapes/objects with mostly uniform colors).
* Related to the last point, the evaluation is completely done on synthesized datasets, and it's not clear how well the model can learn real-world data with much more diverse appearances. Since the method does not require GT annotations, some evaluation on real-world data does not seem too difficult.
* The are some quality issues with the presented view synthesis results. For example, while unobserved regions are understandably blurry, why are observed regions also not sharp? We can see from Fig.10 that adding more views does not help the quality of observed regions any further.
* Training requires multi-view videos. I can't find clarification on whether these need to be from static cameras. Nothing in the model appears to have such a requirement, but if so, that's a strong limitation. Moreover, why can't the model be trained on frames from a single moving camera?
* It's not clearly described how scene editing is done. It says that's enabled by "directly editing the neural groundplan". Does that mean editing the (movable object) feature space? The feature space is not sparse (i.e. locations without objects have non-zero features), so how does cropping in such space work, and why can that make sense?

The evidence to be evaluated is:We similarly use axis-aligned 2D grids of features for self-supervised scene representation via neural rendering, but reconstruct them directly from few or a single 2D image observations.

Some prior work leverages object motion across video frames to learn separate representations for movable foreground and static background in 2D (Kasten et al., 2021; Ye et al., 2022; Bao et al., 2022), while other recent work can also learn 3D representations (Yuan et al., 2021; Tschernezki et al., 2021). Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

The method will be trained on a dataset of multi-view videos with calibrated cameras with wide baselines.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

We now describe training on multi-view video to learn to disentangle static and dynamic components of the scene.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features. The per-frame static groundplans are mean-pooled to obtain a single, time-independent static groundplan.

We train our model on multi-view video, where multi-view information is used to learn the 3D structure, while motion is used to disentangle the static and dynamic components in the scene.

For each time-step, we sample multiple images from different camera views; some of the views are used as input to the method while others are used to compute the loss function. We use the input images to infer static and dynamic groundplans, and use them to render out per-frame query views.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively.

Our method is trained on multi-view observations of dynamic scenes.

CoSY enables generation of a high-quality, path-traced dataset of multi-view videos with large camera baselines. We rendered multi-view observations of \(9000\) scenes with moving cars, sampled using \(15\) background city models and \(95\) car models. We train on \(8000\) scenes, and evenly split the rest into validation and test sets.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene.

As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements. By leveraging multi-view video at training time, we can reconstruct disentangled 3D representations from a single image observation at test time.

* Yariv et al. (2020) Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. _Proc. NeurIPS_, 2020.

* Yuan et al. (2021) Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, and Steven Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In _Proc. CVPR_, pp. 13144-13152, 2021.

**Multi-view input**. In case of multi-view inputs, we average the unprojected latents for all 3D points in the canonical 3D coordinate system for all views. This results, like in the monocular case, in a 3D volume of \(128\times 64\times 16\times 64\) (\(CH\times X\times Y\times Z\)).

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our model supports multi-view, as well as monocular reconstruction. We start our training only using multi-view reconstruction with 5 input views for the first 200 epochs, and then switch to a variable mode where the model is given a varying number of input views ranging between 1-5 views. Thiscurriculum approach helps the network to first learn the relevant scene priors, before learning to complete the 3D structure of the scene.

Our method successfully integrates information across observations into a single, multi-view consistent representation. The background reconstructions improve with more input views, as a result of less uncertainty for the occluded regions.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles.

The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods. To demonstrate the efficacy of the groundplans for frame interpolation, we trained RIFE Huang et al. (2020) on our model trained on the GQN-rooms Eslami et al. (2018) dataset. We generated simple linear motion trajectories for objects over 10 frames.

The scene was rendered from 15 different camera views. We first trained our model on the GQN dataset and used the output dynamic groundplans to train the frame interpolation method.

Firstly, we extracted dynamic groundplan for the samples over different timesteps by passing the multi-view observation through our groundplan generation pipeline.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 5: Single-image reconstruction, disentanglement of static and dynamic objects, and novel view synthesis. Given a single input image, our method can disentangle the observed scene into static and object components based on what the model observed as not-moving and moving in the training data. In these examples, the cars are isolated in the object component as the model was training on video data of cars moving on the road.

Input: Figure 2: Groundplan inference. Given a context image, we first extract a set of CNN features. We unproject the features into 3D and re-sample them at “pillars” on top of the location of groundplan vertices. Pillars are aggregated into groundplan features using a softmax-weighted sum. The resulting 2D grid of features is decomposed into separate dynamic and static groundplans by a 2D CNN. The coordinate-encoding MLP is not visualized in this figure. Please refer to Sec. 3 for details.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 9: Visual quality of views for a given input image.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 3: Learning Static-Dynamic Disentanglement. Given multiple frames of a video, we extract per-frame, compactified, static and dynamic groundplans according to Fig. 2. Static groundplans are pooled into a time-invariant groundplan. We then composite per-frame dynamic and static time-invariant groundplans via differentiable volume rendering. Our model is supervised only via a re-rendering loss on video frames. We encourage the model to explain as much of the scene density as possible with the static groundplan via a sparsity loss on per-frame dynamic volume rendering densities. The surface loss is not visualized here.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find the claim "we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time" to be well-supported by the evidence provided.

The evidence demonstrates:
- Clear methodology for disentangling static and dynamic components using multi-view video training
- Figures showing successful single-image reconstruction with separated static/dynamic elements
- Quantitative details on training data (8000 scenes, multiple views)
- Practical applications demonstrated (scene editing, object manipulation)
- Results validated through novel view synthesis and object manipulation tests

While the review points out some limitations regarding generalization and real-world data, these don't directly contradict the core claim about the training approach and basic capabilities. The claim carefully stays within the scope of what is demonstrated.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time.

The review comment to be evaluated is:

Strength：
1. The conditional neural groundplans proposed are intriguing. This allows efficient processing of scene appearance and geometry directly in 3D, through the self-supervised learning of conditional neural groundplans, a hybrid discrete continuous representation of 3D neural scenes.
2. The proposed disentanglement of static background and movable foreground objects is novel. This approach uses object motion for training an encoder-based system that can reconstruct scenes from a single image instead of decomposing video frames based on scene specifics.
3. The proposed method is promising for understanding 3D from a single image. The dynamic groundplan encodes 3D geometry, allowing us to segment and animate objects in 3D using single images and 3D bounding boxes.

Weaknesses：
1. The biggest weakness is that only results on simple synthetic data are presented. Currently, there are a number of well-annotated 3D driving scenes available. An example is KITTI 360 (https://www.cvlibs.net/datasets/kitti-360/). Could the authors explain why results on the real datasets are not presented? Is it because of some specific annotation (such as BEV correspondence or the number of views) needed by the method? Otherwise, the generalizability on natural scenes and the practical value of the method are questioned.
2. I think there are some ambiguity behind the proposed Static-Dynamic Disentanglement. A definition of Static and Dynamic should be made clear in the proposed framework. A car remaining still in the sequence will be considered as Static. Considering a situation when two moving and static cars looks similar or exactly the same, how can we distinguish from a single image if a car is static or dynamic? The method should mention the above cases as an assumption, or making a clear definition of being Static and Dynamic.
3. Groundplans implicitly assume that objects are aligned on a plane. However, in real life, it is common that scenes/objects are not placed on a flat ground plane (e.g., a curved plane). The ability of persistent 3D scene representation is a little overclaimed, if we consider the implicit assumption behind groundplans.

The evidence to be evaluated is:We similarly use axis-aligned 2D grids of features for self-supervised scene representation via neural rendering, but reconstruct them directly from few or a single 2D image observations.

Some prior work leverages object motion across video frames to learn separate representations for movable foreground and static background in 2D (Kasten et al., 2021; Ye et al., 2022; Bao et al., 2022), while other recent work can also learn 3D representations (Yuan et al., 2021; Tschernezki et al., 2021). Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

The method will be trained on a dataset of multi-view videos with calibrated cameras with wide baselines.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

We now describe training on multi-view video to learn to disentangle static and dynamic components of the scene.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features. The per-frame static groundplans are mean-pooled to obtain a single, time-independent static groundplan.

We train our model on multi-view video, where multi-view information is used to learn the 3D structure, while motion is used to disentangle the static and dynamic components in the scene.

For each time-step, we sample multiple images from different camera views; some of the views are used as input to the method while others are used to compute the loss function. We use the input images to infer static and dynamic groundplans, and use them to render out per-frame query views.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively.

Our method is trained on multi-view observations of dynamic scenes.

CoSY enables generation of a high-quality, path-traced dataset of multi-view videos with large camera baselines. We rendered multi-view observations of \(9000\) scenes with moving cars, sampled using \(15\) background city models and \(95\) car models. We train on \(8000\) scenes, and evenly split the rest into validation and test sets.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene.

As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements. By leveraging multi-view video at training time, we can reconstruct disentangled 3D representations from a single image observation at test time.

* Yariv et al. (2020) Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. _Proc. NeurIPS_, 2020.

* Yuan et al. (2021) Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, and Steven Lovegrove. Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering. In _Proc. CVPR_, pp. 13144-13152, 2021.

**Multi-view input**. In case of multi-view inputs, we average the unprojected latents for all 3D points in the canonical 3D coordinate system for all views. This results, like in the monocular case, in a 3D volume of \(128\times 64\times 16\times 64\) (\(CH\times X\times Y\times Z\)).

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our model supports multi-view, as well as monocular reconstruction. We start our training only using multi-view reconstruction with 5 input views for the first 200 epochs, and then switch to a variable mode where the model is given a varying number of input views ranging between 1-5 views. Thiscurriculum approach helps the network to first learn the relevant scene priors, before learning to complete the 3D structure of the scene.

Our method successfully integrates information across observations into a single, multi-view consistent representation. The background reconstructions improve with more input views, as a result of less uncertainty for the occluded regions.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles.

The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods. To demonstrate the efficacy of the groundplans for frame interpolation, we trained RIFE Huang et al. (2020) on our model trained on the GQN-rooms Eslami et al. (2018) dataset. We generated simple linear motion trajectories for objects over 10 frames.

The scene was rendered from 15 different camera views. We first trained our model on the GQN dataset and used the output dynamic groundplans to train the frame interpolation method.

Firstly, we extracted dynamic groundplan for the samples over different timesteps by passing the multi-view observation through our groundplan generation pipeline.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 5: Single-image reconstruction, disentanglement of static and dynamic objects, and novel view synthesis. Given a single input image, our method can disentangle the observed scene into static and object components based on what the model observed as not-moving and moving in the training data. In these examples, the cars are isolated in the object component as the model was training on video data of cars moving on the road.

Input: Figure 2: Groundplan inference. Given a context image, we first extract a set of CNN features. We unproject the features into 3D and re-sample them at “pillars” on top of the location of groundplan vertices. Pillars are aggregated into groundplan features using a softmax-weighted sum. The resulting 2D grid of features is decomposed into separate dynamic and static groundplans by a 2D CNN. The coordinate-encoding MLP is not visualized in this figure. Please refer to Sec. 3 for details.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 9: Visual quality of views for a given input image.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 3: Learning Static-Dynamic Disentanglement. Given multiple frames of a video, we extract per-frame, compactified, static and dynamic groundplans according to Fig. 2. Static groundplans are pooled into a time-invariant groundplan. We then composite per-frame dynamic and static time-invariant groundplans via differentiable volume rendering. Our model is supervised only via a re-rendering loss on video frames. We encourage the model to explain as much of the scene density as possible with the static groundplan via a sparsity loss on per-frame dynamic volume rendering densities. The surface loss is not visualized here.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I'll evaluate the claim: "In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time."

This claim appears well-stated and accurately reflects the evidence. The paper demonstrates:
- Training on multi-view video data (9000 scenes with 15 camera views)
- Successful disentanglement of static/dynamic components from single images (shown in Figures 1, 5)
- Clear results showing reconstruction quality from single and multiple views (Figure 10)
- Practical applications like object manipulation and scene editing (Figures 11, 12)

While reviewer 2 raises valid concerns about ambiguity in static/dynamic definitions and reviewer 1 notes limitations with synthetic data only, these don't directly invalidate the core claim about the method's capability to perform this disentanglement.

<score>1</score>

Justification:
1. The evidence strongly supports the claim's specific technical assertion about leveraging multi-view videos for training and single-image reconstruction capability
2. The paper provides extensive visual evidence demonstrating this capability works
3. The claim is carefully worded to describe what the method does without overstating its broader applicability
4. While reviewers raise important limitations, they don't contradict this specific technical claim
5. The claim acknowledges the training requirements ("at training time") and test conditions ("from a single image at test time") explicitly


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

* Using the 3D geometry encoded in the dynamic groundplan, we demonstrate single-image 3D instance segmentation and 3D bounding box prediction, as well as 3D scene editing.

The evidence to be evaluated is:Hybrid discrete-continuous neural scene representations offer a compromise by factorizing a dense 3D field into several lower-dimensional representations that are used to condition an MLP (Chan et al., 2022; Chen et al., 2022). In particular, neural groundplans and axis-aligned 2D grids enable high-quality unconditional generation of 3D scenes (DeVries et al., 2021; Chan et al., 2022) as well as reconstruction of 3D geometry from pointclouds (Peng et al., 2020).

The bird's-eye view is generated either directly without 3D inductive biases (Mani et al., 2020), or similar to our proposed approach, by using 3D geometry-driven inductive biases such as unprojection into a volume (Harley et al., 2022; Chen et al., 2022; Roddick et al., 2019), or by generating a 3D point cloud (Philion and Fidler, 2020; Hu et al., 2021).

In contrast, we present a self-supervised conditional groundplan representation, learned only from images via neural rendering. While we show that our self-supervised representation can be used for rich inference tasks using simple heuristics, our method may be extended for more challenging tasks using the techniques developed in prior work.

Our approach is similar in using object motion as cue for disentanglement and multi-view as cue for 3D reconstruction, but uses it as supervision to train an encoder-based approach that enables reconstruction from a _single_ image instead of scene-specific disentanglement from multiple video frames.

Several scene reconstruction methods (Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Nie et al., 2020) use direct supervision to train an object representation and detector to infer an editable 3D scene from a single frame observation.

In this work, we demonstrate that a representation factorized into static and movable 3D regions can serve as a powerful backbone for object discovery.

A neural groundplan is a 2D grid of features aligned with the ground plane of the 3D scene, which we define to be the \(xz-\)plane. A 3D point is decoded by projecting it onto the groundplan and retrieving the corresponding feature vector using bilinear interpolation. This feature is then concatenated with the vertical \(y\)-coordinate of the query point and decoded into radiance and density values via a fully connected network, enabling novel view synthesis using volume rendering (Mildenhall et al., 2020).

Inferring a neural groundplan from one or several images proceeds in three steps: (1) feature extraction, (2) feature unprojection, (3) pillar aggregation. Given a single image \(\mathbf{I}\), we first extract per-pixel features via a CNN encoder to yield a feature tensor \(\mathbf{F}\). We define the camera as the world origin and center the neural groundplan accordingly, approximately aligned with the ground level. The image features are unprojected to a 3D feature volume \(\mathbf{v}\) in contracted world space using the inverse of the contract function defined earlier. We extract the feature at a contracted 3D point \(\mathbf{x}^{\prime}\) in \(\mathbf{v}\) as \(\mathbf{v}(\mathbf{x}^{\prime})=\mathbf{F}[\pi(C^{-1}(\mathbf{x}^{\prime}))]\), where \(C^{-1}(\mathbf{x}^{\prime})\) first maps the contracted point to linear world space and \(\pi(\cdot)\) projects it onto the image plane of the context view using camera extrinsics and intrinsics.

Next, we aggregate each pillar into a point to create the 2D groundplan.

Associated to each 2D vertex of the groundplan is now a set of features \(\{\mathbf{f}_{i}\}_{i=1}^{N}\), where \(N\) is the number of samples along the \(y\)-dimension. We use a "pillar-aggregation" MLP to compute softmax scores as \(\alpha_{i}=P(\mathbf{f}_{i},\mathbf{x}_{i})\), where \(P(\cdot)\) denotes the MLP and \(\mathbf{x}_{i}\) is the linear coordinate of the \(i\)-th point on the pillar. Finally, the features are aggregated by computing the weighted sum of the features, \(\mathbf{g}=\sum_{i}\alpha_{i}\mathbf{f}_{i}\).

Furthermore, we describe a method for performing self-supervised 3D object discovery and 3D bounding box prediction using the geometry encoded in the dynamic groundplan representation.

We leverage the fact that objects move in the given multi-view videos as the training signal. We pick two random frames of a video. For each frame, we infer an _entangled_ neural groundplan as described in the previous section. Features in this entangled neural groundplan parameterize both static and dynamic features of the scene, for instance, a car as well as the road below it. We feed this groundplan into a fully convolutional 2D network, which disentangles it into two separate groundplans containing _static_ and _dynamic_ features.

Our formulation yields a model that maps a single image to two radiance fields, parameterizing static and dynamic 3D regions respectively.

We now perform a search for connected components in the dynamic neural groundplan to perform 3D instance-level segmentation, monocular 3D bounding box prediction, and the extraction of object-centric 3D representations. Specifically, given a dynamic groundplan, we first sample points in a 3D grid around the groundplan origin and decode their densities. We now perform conventional connected-component labeling in the groundplan space using accumulated density values, identifying the disconnected dynamic objects. We perform 2D instance-level segmentation for a queried viewpoint using volume rendering based on the densities expressed by the dynamic groundplan and assigning a color to the points corresponding to each of the identified objects, see Fig. 1 for an example. Furthermore, we compute the smallest box that contains the connected component to get a 3D bounding box for each identified object. Finally, we crop tiles of the dynamic groundplan that belong to a given object instance to obtain object-centric 3D representations, enabling editing of 3D scenes such as deletion, insertion, and rigid-body transformation of objects.

We demonstrate that our method infers a 3D scene representation from a single image while disentangling static and dynamic components of the scene into static and dynamic groundplans respectively. We then show that connected components analysis suffices to leverage the densities in the dynamic groundplan for instance-level segmentation, bounding box prediction, and scene editing.

We present novel views rendered from groundplans inferred from a single image from CLEVR and CoSY. For single-shot 3D reconstruction and novel view synthesis, we compare against PixelNeRF (Yu et al., 2020), a state-of-the-art single-image 3D reconstruction method, and uORF (Yu et al., 2022), state-of-the-art unsupervised object-centric 3D reconstruction method.

Given only a single image, our method computes separate static and dynamic groundplans that can be used to individually render the static and movable parts of the scene respectively. Fig. 5 shows results on single-image reconstruction of static and movable scene elements.

The separate reconstruction of movable scene components in the dynamic groundplan enables object detection via instance-level segmentation and bounding box prediction. Fig. 6 presents the instance-level segmentation and 3D bounding box prediction results of the proposed 3D object discovery via connected component discovery using the density inferred using the dynamic groundplan from the bird's-eye view.

Instance-level segmentation, dynamic-static disentanglement, and 3D bounding boxes enable straight-forward 3D editing, such as translation, rotation, deletion, and insertion of individual objects in the scene. Objects can be rotated by arbitrary angles by simple bilinear interpolation of the groundplan features (refer to Appendix B.3). As the dynamic groundplan does not encode static scene regions such as the street below cars, cars can easily be moved from one scene to another. Fig. 8 provides scene editing results of our method. Note that such editing is difficult with methods that lack a persistent 3D representation, such as PixelNeRF.

Our paper demonstrates self-supervised learning of 3D scene representations that are disentangled into movable and immovable scene elements.

We show the potential of neural ground plans as a representation that may enable data-efficient solutions to downstream processing of the 3D scene, such as completion, instance-level segmentation, 3D bounding box prediction, and 3D scene editing.

This shallow CNN outputs groundplan of shape \(256\times 256\times 256\), in which the first 128 channels of the feature tensor are attributed as the static groundplan and the rest of the 128 channels are used as the dynamic groundplan for that timestep.

Our method performs project of a 3D point on the image plane to get the corresponding feature tensor. This is performed using the intrinsic matrix of the camera. For a given 3D point in camera coordinates \(\mathbf{x_{c}}\) and intrinsic matrix \(\mathbf{K}\), the resulting 2D point on the image plane is computed as \(\mathbf{Kx}\). For projecting a 3D query point \(\mathbf{x}\) on the groundplan, we use grid sample using \((x,z)\)-coordinates of the 3D query points \(\mathbf{x}\).

Our model supports multi-view, as well as monocular reconstruction.

For localization of objects, we consider the dynamic groundplan and render it from an orthographic bird's-eye view. Along with the bird's-eye view RGB image, we also generate the occupancy map (per-pixel accumulated alpha) as shown in the manuscript in Fig. 5.

Given the localization in the orthographic bird's-eye view, we can find the height of the objects by computing the depth within each region. This information gives us a 3D bounding box around each of the localized object. The instance level segmentation is produced via volume rendering, by overriding the RGB values within the detected region to a chosen color for the object.

Scene editing is performed by editing the dynamic groundplan. Once the different objects have been localized in the dynamic groundplan, we can edit the dynamic groundplan to carry out object deletion, insertion, and rearrangement. Localizing an object gives us its features in the spatial region of the groundplan used for rendering. To delete the object, we replace the features in this regions with features from the dynamic groundplan that encode zero density. For inserting an object at a given location, we find the corresponding \((x,z)\) location in the dynamic groundplan and set the features at that location to the features corresponding to the object. Rearrangement of objects can be seen as a combination of deletion and insertion where we first delete the object from the existing location, followed by inserting the object at the new location in a possibly new orientation. To perform rotation of an object, we rotate the patch of features that correspond to the object to be rotated.

Fig. 11 provides rendered images from edited groundplans where the cars are rotated at different angles. Note that the representation only allows for rotations in the \(xz\)-plane. The dynamic groundplan can be further used to perform motion interpolation in 3D, using optical flow for groundplan interpolation prediction using off-the-shelf, state-of-the-art optical flow and frame interpolation methods.

The proposed method succeeds at inferring the correct object motion, and enables novel view synthesis through space and time. We strongly encourage the readers to refer to our supplemental webpage for more novel-view synthesis, static-dynamic disentanglement, localization, and scene editing results, as well as video comparisons with the state of the art.

Input: Figure 1: Given a single image, our model infers separate 3D representations for static and dynamic scene elements, enabling high-quality novel view synthesis with plausible completion, unsupervised instance-level segmentation, 3D bounding box prediction, 3D scene editing, and extraction of objectcentric 3D representations. Our model is trained self-supervised using unlabeled multi-view videos.

Input: Figure 10: Resolving uncertainty with increasing number of context views Our model can take a variable number of viewpoints of a scene as input. We find that the model gets a good prior on the object from a single view but struggles to reconstruct high-quality details in the unobserved regions of the background. This uncertainty gets resolved with more input views.

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 12: Temporal 3D scene interpolation for novel view synthesis in space and time.

Input: Figure 6: Object Localization. Given a single input image, we can use our inferred 3D representation of the scene to compute (a) bird’s-eye view rendering, (b) object localization in the bird’s-eye view space, (c) instance-level segmentation, and (d) 3D bounding box prediction.

Input: Figure 7: Qualitative Comparison for instance-level segmentation. Qualitative comparison of instance-level segmentation result with uORF (Yu et al., 2022).

Input: Figure 11: Scene editing with rotated objects. We present images of scenes with edited groundplans where cars are rotated at various angles in the ground plane.

Input: Figure 8: Object-centric representations and scene editing. The proposed static-dynamic neural groundplans enables object discovery using connected components labeling on the density of objects encoded in the dynamic groundplan (left). This enables straight-forward scene editing such as deletion, addition, or rigid-body transformation via directly editing the neural groundplan (right).

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the evidence strongly supports the claim that the method can perform single-image 3D instance segmentation, 3D bounding box prediction, and 3D scene editing using the dynamic groundplan representation. The evidence includes:

- Detailed technical explanation of how the 3D geometry is encoded and used
- Multiple figures demonstrating the capabilities (Figs 1, 6, 8) showing instance segmentation, bounding boxes, and scene editing
- Comprehensive results across different scenes and objects
- Clear methodology for extracting 3D information from the groundplan representation

The claim appears to be well-stated and accurately reflects what the evidence demonstrates without exaggeration.

<score>1</score>