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:

This further enables intuitive 3D editing of the scene.

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).

Resulting object representations may be decoded into object-centric 3D representations and composed for novel view synthesis (Yu et al., 2022; Smith et al., 2022; Elich et al., 2022; Chen et al., 2021; Bear et al., 2020; Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Du et al., 2021).

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.

Points \(\mathbf{x}\) within a radius \(r_{\text{inner}}\) around the groundplan origin remain unaffected, but points outside this radius are contracted. For any 3D point \(\mathbf{x}\), the contracted 3D coordinate can be computed as \(\mathbf{x}^{\prime}=C(\mathbf{x})=((1+k)-k/||\mathbf{u}||)(\mathbf{u}/|| \mathbf{u}||)r_{\text{inner}}\), where \(\mathbf{u}=\mathbf{x}/r_{\text{inner}}\), and \(k\) is a hyperparameter which controls the size of the contracted region. Note that \(C\) is invertible, such that \(\mathbf{x}=C^{-1}(\mathbf{x}^{\prime})\) is a function that takes a 3D point in contracted space \(\mathbf{x}^{\prime}\) to the original 3D point \(\mathbf{x}\) in linear space.

To render a scene using the disentangled static and dynamic groundplans, we first decode query points using both groundplans, yielding _two_ sets of (density, color) values for each point. We use the compositing operation proposed by Yuan et al. (2021) to compose the contribution from static and dynamic components along the ray. Given the color and density for static \((\mathbf{c}^{S},\sigma^{S})\) and dynamic \((\mathbf{c}^{D},\sigma^{D})\) parts, the density of the combined scene is calculated as \(\sigma^{S}+\sigma^{D}\). The color at the sampled point is computed as a weighted linear combination \(w^{S}\mathbf{c}^{S}+w^{D}\mathbf{c}^{D}\), where \(w^{S}=(1-\exp(-\delta\sigma^{S}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), \(w^{D}=(1-\exp(-\delta\sigma^{D}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), and \(\delta\) is the distance between adjacent samples on the camera ray.

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.

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 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.

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.

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).

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.

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.

Learning object-compositional neural radiance field for editable scene rendering.

We populate a coarse 3D feature volume of shape \(64\times 16\times 64\) with 128-dimensional latents for points in the camera frustum by bilinearly sampling the aggregated encoder feature tensor. This volume is processed as explained in the main paper: a coordinate encoding MLP transforms each feature into a new \(128\) dimensional feature vector. This MLP has two hidden layers with 128 hidden units each and a ReLU activation after each hidden layer.

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 \(\mathbf{x}\).

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.

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.

In addition to the rendering capabilities of CoSY, we add the ability to move cars over timesteps. Given the location and direction of the movement (direction where the car is facing), we change the location of the car over 10 timesteps to a sampled total translation from range 2-4 m.

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. 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 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 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 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 "This further enables intuitive 3D editing of the scene" and the provided evidence, I find this claim to be well-supported. The evidence demonstrates multiple concrete capabilities for 3D scene editing, including:

- Object deletion, insertion, and rearrangement through groundplan editing
- Rotation of objects in the xz-plane through feature patch rotation
- Instance-level segmentation and bounding box detection enabling object manipulation
- Separation of static and dynamic elements allowing targeted editing
- Visual examples in Figures 8 and 11 showing successful editing operations

The editing capabilities are well-documented with specific technical details about implementation and clear visual results demonstrating the functionality. While "intuitive" is somewhat subjective, the straightforward nature of the editing operations described suggests this characterization is reasonable.

<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:

This further enables intuitive 3D editing of the scene.

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: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).

Resulting object representations may be decoded into object-centric 3D representations and composed for novel view synthesis (Yu et al., 2022; Smith et al., 2022; Elich et al., 2022; Chen et al., 2021; Bear et al., 2020; Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Du et al., 2021).

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.

Points \(\mathbf{x}\) within a radius \(r_{\text{inner}}\) around the groundplan origin remain unaffected, but points outside this radius are contracted. For any 3D point \(\mathbf{x}\), the contracted 3D coordinate can be computed as \(\mathbf{x}^{\prime}=C(\mathbf{x})=((1+k)-k/||\mathbf{u}||)(\mathbf{u}/|| \mathbf{u}||)r_{\text{inner}}\), where \(\mathbf{u}=\mathbf{x}/r_{\text{inner}}\), and \(k\) is a hyperparameter which controls the size of the contracted region. Note that \(C\) is invertible, such that \(\mathbf{x}=C^{-1}(\mathbf{x}^{\prime})\) is a function that takes a 3D point in contracted space \(\mathbf{x}^{\prime}\) to the original 3D point \(\mathbf{x}\) in linear space.

To render a scene using the disentangled static and dynamic groundplans, we first decode query points using both groundplans, yielding _two_ sets of (density, color) values for each point. We use the compositing operation proposed by Yuan et al. (2021) to compose the contribution from static and dynamic components along the ray. Given the color and density for static \((\mathbf{c}^{S},\sigma^{S})\) and dynamic \((\mathbf{c}^{D},\sigma^{D})\) parts, the density of the combined scene is calculated as \(\sigma^{S}+\sigma^{D}\). The color at the sampled point is computed as a weighted linear combination \(w^{S}\mathbf{c}^{S}+w^{D}\mathbf{c}^{D}\), where \(w^{S}=(1-\exp(-\delta\sigma^{S}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), \(w^{D}=(1-\exp(-\delta\sigma^{D}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), and \(\delta\) is the distance between adjacent samples on the camera ray.

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.

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 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.

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.

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).

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.

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.

Learning object-compositional neural radiance field for editable scene rendering.

We populate a coarse 3D feature volume of shape \(64\times 16\times 64\) with 128-dimensional latents for points in the camera frustum by bilinearly sampling the aggregated encoder feature tensor. This volume is processed as explained in the main paper: a coordinate encoding MLP transforms each feature into a new \(128\) dimensional feature vector. This MLP has two hidden layers with 128 hidden units each and a ReLU activation after each hidden layer.

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 \(\mathbf{x}\).

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.

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.

In addition to the rendering capabilities of CoSY, we add the ability to move cars over timesteps. Given the location and direction of the movement (direction where the car is facing), we change the location of the car over 10 timesteps to a sampled total translation from range 2-4 m.

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. 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 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 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 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 evidence, the claim "This further enables intuitive 3D editing of the scene" appears to be well-stated. The evidence demonstrates multiple capabilities for scene editing, including:

- Object deletion, addition, and rearrangement through groundplan manipulation
- Rotation of objects in the xz-plane
- Extraction of object-centric 3D representations
- Dynamic object motion interpolation

The figures (especially Fig. 8) show concrete examples of these editing capabilities working effectively. The method's ability to disentangle static and dynamic elements, combined with its neural groundplan representation, provides a clear technical foundation for these editing operations. While there are some limitations (e.g., rotations only in the ground plane), the claim reasonably characterizes the demonstrated editing capabilities without overstating them.

<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:

This further enables intuitive 3D editing of the scene.

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: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).

Resulting object representations may be decoded into object-centric 3D representations and composed for novel view synthesis (Yu et al., 2022; Smith et al., 2022; Elich et al., 2022; Chen et al., 2021; Bear et al., 2020; Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Du et al., 2021).

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.

Points \(\mathbf{x}\) within a radius \(r_{\text{inner}}\) around the groundplan origin remain unaffected, but points outside this radius are contracted. For any 3D point \(\mathbf{x}\), the contracted 3D coordinate can be computed as \(\mathbf{x}^{\prime}=C(\mathbf{x})=((1+k)-k/||\mathbf{u}||)(\mathbf{u}/|| \mathbf{u}||)r_{\text{inner}}\), where \(\mathbf{u}=\mathbf{x}/r_{\text{inner}}\), and \(k\) is a hyperparameter which controls the size of the contracted region. Note that \(C\) is invertible, such that \(\mathbf{x}=C^{-1}(\mathbf{x}^{\prime})\) is a function that takes a 3D point in contracted space \(\mathbf{x}^{\prime}\) to the original 3D point \(\mathbf{x}\) in linear space.

To render a scene using the disentangled static and dynamic groundplans, we first decode query points using both groundplans, yielding _two_ sets of (density, color) values for each point. We use the compositing operation proposed by Yuan et al. (2021) to compose the contribution from static and dynamic components along the ray. Given the color and density for static \((\mathbf{c}^{S},\sigma^{S})\) and dynamic \((\mathbf{c}^{D},\sigma^{D})\) parts, the density of the combined scene is calculated as \(\sigma^{S}+\sigma^{D}\). The color at the sampled point is computed as a weighted linear combination \(w^{S}\mathbf{c}^{S}+w^{D}\mathbf{c}^{D}\), where \(w^{S}=(1-\exp(-\delta\sigma^{S}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), \(w^{D}=(1-\exp(-\delta\sigma^{D}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), and \(\delta\) is the distance between adjacent samples on the camera ray.

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.

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 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.

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.

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).

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.

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.

Learning object-compositional neural radiance field for editable scene rendering.

We populate a coarse 3D feature volume of shape \(64\times 16\times 64\) with 128-dimensional latents for points in the camera frustum by bilinearly sampling the aggregated encoder feature tensor. This volume is processed as explained in the main paper: a coordinate encoding MLP transforms each feature into a new \(128\) dimensional feature vector. This MLP has two hidden layers with 128 hidden units each and a ReLU activation after each hidden layer.

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 \(\mathbf{x}\).

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.

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.

In addition to the rendering capabilities of CoSY, we add the ability to move cars over timesteps. Given the location and direction of the movement (direction where the car is facing), we change the location of the car over 10 timesteps to a sampled total translation from range 2-4 m.

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. 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 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 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 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:
Based on my review of the claim "This further enables intuitive 3D editing of the scene" and the provided evidence, I find this claim to be well-supported. The evidence demonstrates multiple concrete capabilities for 3D scene editing, including:

- Object deletion, insertion, and rearrangement through groundplan manipulation
- Rotation of objects in the xz-plane through feature patch rotation
- Instance-level segmentation and bounding box detection enabling object-level editing
- Multiple figures showing successful editing results (Fig. 8, 11)

The claim is modest in scope, using "enables" rather than stronger claims about perfection or state-of-the-art performance. The editing capabilities shown are indeed intuitive, working directly with the groundplan representation.

<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:

This further enables intuitive 3D editing of the scene.

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: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).

Resulting object representations may be decoded into object-centric 3D representations and composed for novel view synthesis (Yu et al., 2022; Smith et al., 2022; Elich et al., 2022; Chen et al., 2021; Bear et al., 2020; Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Du et al., 2021).

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.

Points \(\mathbf{x}\) within a radius \(r_{\text{inner}}\) around the groundplan origin remain unaffected, but points outside this radius are contracted. For any 3D point \(\mathbf{x}\), the contracted 3D coordinate can be computed as \(\mathbf{x}^{\prime}=C(\mathbf{x})=((1+k)-k/||\mathbf{u}||)(\mathbf{u}/|| \mathbf{u}||)r_{\text{inner}}\), where \(\mathbf{u}=\mathbf{x}/r_{\text{inner}}\), and \(k\) is a hyperparameter which controls the size of the contracted region. Note that \(C\) is invertible, such that \(\mathbf{x}=C^{-1}(\mathbf{x}^{\prime})\) is a function that takes a 3D point in contracted space \(\mathbf{x}^{\prime}\) to the original 3D point \(\mathbf{x}\) in linear space.

To render a scene using the disentangled static and dynamic groundplans, we first decode query points using both groundplans, yielding _two_ sets of (density, color) values for each point. We use the compositing operation proposed by Yuan et al. (2021) to compose the contribution from static and dynamic components along the ray. Given the color and density for static \((\mathbf{c}^{S},\sigma^{S})\) and dynamic \((\mathbf{c}^{D},\sigma^{D})\) parts, the density of the combined scene is calculated as \(\sigma^{S}+\sigma^{D}\). The color at the sampled point is computed as a weighted linear combination \(w^{S}\mathbf{c}^{S}+w^{D}\mathbf{c}^{D}\), where \(w^{S}=(1-\exp(-\delta\sigma^{S}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), \(w^{D}=(1-\exp(-\delta\sigma^{D}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), and \(\delta\) is the distance between adjacent samples on the camera ray.

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.

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 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.

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.

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).

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.

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.

Learning object-compositional neural radiance field for editable scene rendering.

We populate a coarse 3D feature volume of shape \(64\times 16\times 64\) with 128-dimensional latents for points in the camera frustum by bilinearly sampling the aggregated encoder feature tensor. This volume is processed as explained in the main paper: a coordinate encoding MLP transforms each feature into a new \(128\) dimensional feature vector. This MLP has two hidden layers with 128 hidden units each and a ReLU activation after each hidden layer.

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 \(\mathbf{x}\).

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.

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.

In addition to the rendering capabilities of CoSY, we add the ability to move cars over timesteps. Given the location and direction of the movement (direction where the car is facing), we change the location of the car over 10 timesteps to a sampled total translation from range 2-4 m.

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. 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 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 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 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:
Based on my review of the evidence and reviewer comments, I will assess whether the claim "This further enables intuitive 3D editing of the scene" is well-stated or overstated.

Summary: The evidence demonstrates clear capabilities for 3D scene editing through the neural groundplan representation, including object deletion, insertion, rotation, and rearrangement as shown in Figure 8. The paper provides technical details about how these edits are implemented through feature manipulation. However, there are some limitations noted in the reviewer comments - the editing process is not clearly described and questions remain about how cropping works in the non-sparse feature space. While editing is possible, calling it "intuitive" may slightly overstate the user experience without more evidence of interface usability.

<score>2</score>

Justification:
1. The evidence strongly supports basic 3D editing capabilities:
- "Scene editing is performed by editing the dynamic groundplan" with specific methods described for deletion, insertion and rearrangement
- Multiple figures demonstrate successful editing results
- The technical implementation is explained in detail

2. However, "intuitive" is somewhat overstated because:
- The reviewer notes "It's not clearly described how scene editing is done"
- Questions remain about the technical details of feature space manipulation
- No evidence is presented about the user interface or editing workflow
- Limited to specific types of edits (rotation only in xz-plane)

So while the core claim about enabling 3D editing is well-supported, characterizing it as "intuitive" goes slightly beyond what's demonstrated, warranting a score of 2 (minor overstatement).


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:

This further enables intuitive 3D editing of the scene.

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: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).

Resulting object representations may be decoded into object-centric 3D representations and composed for novel view synthesis (Yu et al., 2022; Smith et al., 2022; Elich et al., 2022; Chen et al., 2021; Bear et al., 2020; Zakharov et al., 2020; Zekharov et al., 2020; Beker et al., 2020; Du et al., 2021).

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.

Points \(\mathbf{x}\) within a radius \(r_{\text{inner}}\) around the groundplan origin remain unaffected, but points outside this radius are contracted. For any 3D point \(\mathbf{x}\), the contracted 3D coordinate can be computed as \(\mathbf{x}^{\prime}=C(\mathbf{x})=((1+k)-k/||\mathbf{u}||)(\mathbf{u}/|| \mathbf{u}||)r_{\text{inner}}\), where \(\mathbf{u}=\mathbf{x}/r_{\text{inner}}\), and \(k\) is a hyperparameter which controls the size of the contracted region. Note that \(C\) is invertible, such that \(\mathbf{x}=C^{-1}(\mathbf{x}^{\prime})\) is a function that takes a 3D point in contracted space \(\mathbf{x}^{\prime}\) to the original 3D point \(\mathbf{x}\) in linear space.

To render a scene using the disentangled static and dynamic groundplans, we first decode query points using both groundplans, yielding _two_ sets of (density, color) values for each point. We use the compositing operation proposed by Yuan et al. (2021) to compose the contribution from static and dynamic components along the ray. Given the color and density for static \((\mathbf{c}^{S},\sigma^{S})\) and dynamic \((\mathbf{c}^{D},\sigma^{D})\) parts, the density of the combined scene is calculated as \(\sigma^{S}+\sigma^{D}\). The color at the sampled point is computed as a weighted linear combination \(w^{S}\mathbf{c}^{S}+w^{D}\mathbf{c}^{D}\), where \(w^{S}=(1-\exp(-\delta\sigma^{S}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), \(w^{D}=(1-\exp(-\delta\sigma^{D}))/(1-\exp(-\delta(\sigma^{S}+\sigma^{D}))\), and \(\delta\) is the distance between adjacent samples on the camera ray.

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.

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 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.

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.

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).

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.

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.

Learning object-compositional neural radiance field for editable scene rendering.

We populate a coarse 3D feature volume of shape \(64\times 16\times 64\) with 128-dimensional latents for points in the camera frustum by bilinearly sampling the aggregated encoder feature tensor. This volume is processed as explained in the main paper: a coordinate encoding MLP transforms each feature into a new \(128\) dimensional feature vector. This MLP has two hidden layers with 128 hidden units each and a ReLU activation after each hidden layer.

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 \(\mathbf{x}\).

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.

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.

In addition to the rendering capabilities of CoSY, we add the ability to move cars over timesteps. Given the location and direction of the movement (direction where the car is facing), we change the location of the car over 10 timesteps to a sampled total translation from range 2-4 m.

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. 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 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 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 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 "This further enables intuitive 3D editing of the scene" and the provided evidence, I find that this claim is relatively well-stated based on several key pieces of evidence:

The evidence demonstrates concrete capabilities for 3D scene editing, including:
- Deletion, insertion, and rearrangement of objects through groundplan editing
- Object rotation in the xz-plane through feature patch rotation
- Instance-level segmentation and 3D bounding box detection
- Extraction of object-centric 3D representations

However, there are some limitations noted in the review comments:
- Only tested on synthetic data, not real scenes
- Limited to objects aligned on a plane
- Rotations restricted to the ground plane

While the editing capabilities are clearly demonstrated, calling it "intuitive" may be slightly optimistic given these constraints. Overall, I would rate this as a 2 - generally well-stated but with minor overstatement.

<score>2</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:

Since neural groundplans are 2D grids of features without perspective camera distortion, shift-equivariant processing using inexpensive 2D CNNs effectively completes occluded regions.

The evidence to be evaluated is: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). 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.

Prior work uses ground-plane 2D grids as representations for object detection and segmentation (Saha et al., 2022; Harley et al., 2022; Philion and Fidler, 2020; Reiher et al., 2020; Roddick et al., 2019), layout generation and completion (Cao and de Charette, 2022; Jeong et al., 2022; Mani et al., 2020; Yang et al., 2021), and next-frame prediction (Hu et al., 2021; Zieba 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).

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.

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 \(\mathbf{F}\).

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.

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.

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.

Note that our approach is compatible with prior work leveraging slot attention (Locatello et al., 2020; Kipf et al., 2021) and other inference modules, which can be run on the disentangled dynamic groundplan which, in contrast to image space, enables shift-equivariant processing free from perspective distortion and encodes 3D structure.

Note that our model produces novel views with plausible completions of parts of the scene that are unobserved in the context image such as the back-side of objects. As expected from a non-generative method, regions that are entirely unconstrained such as occluded parts of the background (such as buildings) are blurry.

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.

Note that cars are reliably encoded by the dynamic groundplan, and our method inpaints regions occluded in the input view.

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

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.

Following PixelNeRF, we use the first 4 convolutional blocks of the ResNet34 architecture to create a feature grid. For a \(128\times 128\) resolution image, this results in a feature grid of \(64\times 64\) resolution with \(512\) features per point. We further use a shallow CNN to reduce the number of feature channels, and to aggregate information along the y-axis.

This feature aggregation CNN has two convolutional hidden layers with 256 hidden units and a stride of 2 along the height of the feature tensor. The output is a \(16\times 64\) resolution feature grid with \(128\) features per point.

To construct an entangled groundplan, we aggregate the 3D feature volume into a 2D groundplan using a pillar aggregation MLP.

We use a shallow CNN with 4 hidden convolutional layers to disentangle the entangled groundplan of shape \(128\times 64\times 64\). The first two convolutional layers have 128 hidden units with kernel size of 3, stride of 1, and reflection padding of 1. The last two convolutional layers comprise of 256 hidden units with the same configuration for other parameters.

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 \(\mathbf{x}\).

For localization of objects, we consider the dynamic groundplan and render it from an orthographic bird's-eye view.

Since the groundplans have a spatial extent of \(256\times 256\), the phplic occupancy map is rendered at the same resolution.

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.

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:
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 appears somewhat overstated. While the evidence shows that neural groundplans can effectively represent and process 2D grid features and handle some occlusions, particularly for dynamic objects like cars, the completions are not always high quality. The paper explicitly states that "regions that are entirely unconstrained such as occluded parts of the background (such as buildings) are blurry." The claim doesn't acknowledge these limitations and overstates the effectiveness of the completion capabilities, though the shift-equivariant processing aspect is supported by the evidence.

<score>3</score>

The evidence shows:
1. Success in handling some occlusions, particularly for dynamic objects
2. Limitations in completing entirely occluded regions (blurry results)
3. Support for shift-equivariant processing due to the 2D grid structure
4. Effective representation but not necessarily effective completion in all cases
5. The figure shows successful object manipulation but doesn't demonstrate comprehensive occlusion handling

The claim would be better stated if it acknowledged the varying effectiveness of completion depending on the type of occlusion and scene elements.