1114 Research  Furniture Removal Results. Left is the input image with
furniture. Right is our predicted empty room.

# Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes

Prakhar Kulshreshtha
Geomagical Labs e-mail<EMAIL_ADDRESS>Inc  Konstantinos-Nektarios
Lianos
Geomagical Labs e-mail<EMAIL_ADDRESS>Inc  Brian Pugh
Geomagical Labs e-mail<EMAIL_ADDRESS>Inc  Salma Jiddi
Geomagical Labs e-mail<EMAIL_ADDRESS>Inc

###### Abstract

We address the problem of detecting and erasing furniture from a wide angle
photograph of a room. Inpainting large regions of an indoor scene often
results in geometric inconsistencies of background elements within the inpaint
mask. To address this problem, we utilize perceptual information (e.g.
instance segmentation, and room layout) to produce a geometrically consistent
empty version of a room. We share important details to make this system
viable, such as per-plane inpainting, automatic rectification, and texture
refinement. We provide detailed ablation along with qualitative examples,
justifying our design choices. We show an application of our system by
removing real furniture from a room and redecorating it with virtual
furniture.

Augmented RealityDiminished RealityComputer visionInpainting

Introduction

The ability to remove objects from a scene is a common task in applications
like image editing, Augmented Reality, and Diminished Reality [5, 25]. The
general problem of image inpainting has seen many improvement over the past
few decades in both classical and deep learning approaches [6, 40, 45, 32].
Modern inpainting techniques work incredibly well for small to medium sized
regions [32], but still struggle to produce convincing results for larger
missing segments. For these regions, the texture and structure from
surrounding areas fail to propagate in a visually pleasing, and physically
plausible way. Inpainting large regions requires geometric, texture, and
lighting consistency to produce convincing results. State-of-the-art
inpainting networks like [32] often fail to complete large global structures,
like the plausible continuation of walls, ceilings, and floors in an indoor
scene.

In this work, we address the challenges of inpainting large regions of an
indoor scene, and propose a system for synthesizing a view of the room with
all objects removed. To do this, we introduce multiple novel steps that are
not present in generic inpainting systems. The contribution of this work is as
follows:

  * •

A strategy for applying inpainting networks trained on in-the-wild images on
rectified planes.

  * •

Refining the inpainted texture using a combination of offline training and
run-time optimization.

  * •

An end-to-end system for automatically detecting and erasing furniture from a
room, and its application as an interior design product (Layout Aware
Inpainting for Automated Furniture Removal in Indoor Scenes).

##  1 Related Work

Figure 1: System Overview. Input data is fed through an Input Processor (A) to
generate intermediate perceptual artifacts like segmentation map and room
layout. This perceptual data is fed into the furniture eraser engine (B) where
image inpainting is performed on each independent rectified plane in the
scene. The results are blended together to obtain an image of the empty room.

A number of approaches have been proposed for solving image inpainting,
including color-diffusion-based methods [33, 7], patch-based techniques [6,
10], convolutional neural networks [23, 39, 40, 26, 32], and diffusion models
[30]. Deep Neural Networks (DNN) have been observed to perform well at
inpainting small and medium-sized holes [32, 45] because of their ability to
encode both local and global context. Improving the receptive field of a
neural network to improve performance has been the focus of developments like
fourier convolutions[32], transformers[21], and diffusion models[30]. Despite
these improvements, these networks still struggle to inpaint an indoor scene
with large unknown regions (Figure 2).

Figure 2: From left to right: input image, masked image, and then inpainting
results using [32], [45] and [30] respectively. The results are blurry, color
inconsistent, and hallucinate new unwanted structure.

Inpainting can be constrained using edges[26], semantic segmentation[28], and
patches from known regions[38]. [15] utilizes planar information to produce
perspective corrected patches prior to applying patched-based inpainting. [18]
performs patch-based per-plane inpainting, but it requires user input to
determine the resolution of the rectified image. [13] utilizes room layout
information to empty a spherical panorama image of a furnished room. Their
system was trained end-to-end on layout and empty room data, which are
difficult to collect. [42] incorporates lighting and geometry constraints by
coupling an intrinsic image decomposition network, a differentiable shadow
removal module, and an inpainting network.

Directly trying to solve the same problem, [41] also estimates an empty
version of a room. Their system recovers the light sources and the surface
reflectance of various elements of the scene, but it has been shown to only
work on rooms with relatively simple texture and geometry. A completely
different approach to indoor inpainting is to estimate the room with a CAD
model[16]. While IM2CAD maintains the layout of the room, it replaces floors
with generic wood texture, and walls with a median color that may not
accurately represent the scene.

In our system, we constrain inpainting of an indoor scene by exploiting the 3D
planar layout of a room. Similar to [18], we perform inpainting on a per-
rectified-plane basis. However, we use a DNN instead of a patch-based
algorithm to perform the actual inpainting. Unlike [18], we do not need to
explicitly set the rectification resolution because it is tied to the training
resolution of the inpainting network. Our system is modular, consisting of
several independent neural networks and classical computer vision algorithms,
allowing each component to be independently updated as new improvements are
developed in common computer vision tasks. Our inpainting network is trained
on in-the-wild indoor and outdoor images, without needing any manual
annotations.

##  2 System Overview

Our system is composed of an input processor and a furniture eraser engine
(see Figure 1). The input processor parses the input image using existing
methods to obtain perceptual cues such as segmentation masks [43, 4, 12, 8]
and the room layout [22]. While we recognize that extracting any of these
perceptual cues is not a trivial task, in this work, we assume these cues to
be a part of the input, and explicitly focus on the inpainting part of our
system. In the subsequent subsections, we describe how the furniture eraser
engine utilize these cues for inpainting.

###  2.1 Inpainting mask

The furniture eraser engine uses instance segmentation to identify all the
objects in the scene. The union of all the object masks becomes the inpainting
mask for our task of emptying the room (see Fig. 3). Our goal is to replace
all the pixels in this mask with the background texture.

Figure 3: Utilizing the semantic information, we extract an inpainting mask
(blue regions) comprising of all the objects in the scene.

###  2.2 Plane-wise inpainting

We observe that inpainting an indoor scene suffers from two major problems:
perspective distortion, and context mixing from different texture regions of
an image. These problems are especially pronounced when we remove large
objects from a scene [32, 45]. We utilize the room layout information to solve
this problem. Each wall and floor of the room can be represented as a plane in
3D space. Given the 3D equation of a plane, along with its binary occupancy
mask in the image, we can rectify the plane to become fronto-parallel to the
camera (Figure 4). We determine the resolution of the rectified plane based on
the native training resolution of the inpainting network, since a neural
inpainting network achieves optimal performance around the resolution at which
it was originally trained[20].

Figure 4: Rectification of the floor plane before inpainting. The blue areas
in the second image show the inpainting mask to the neural network.

###  2.3 Utilizing a pretrained DNN

In this section, we discuss how to utilize a Neural Network pretrained on the
task of image inpainting in the wild, for inpainting indoor scenes. As shown
in third row in Figure 9, directly using a network on simple perspective
images results in a very blurry output. This happens because the receptive
field of the network is limited, which makes it challenging to complete the
geometry and semantic regions in large holes. To overcome these issues, we
inpaint each rectified plane separately. The inpaint mask is defined to be the
union of furniture mask and the unknown area (since rectification would
introduce some out-of-frame pixels in the rectified view). This image-mask
pair is input to the inpainting network, and then output is unrectified to
fill in the missing pixels of that plane. Per-plane inpainting substantially
improves the inpainting quality (fourth row of Figure 9).

###  2.4 Texture refinement

We observe that inpainting networks trained on the Places2[46] dataset often
infills large regions with an undesired gray texture (See Fig. 9). To remedy
this, we prepare a more diverse mixture of around 1 million RGB images, from
SUN-RGBD[47, 31, 17, 36], Diode[34], Unsplash[1], Hypersim[29],
OpenImages[19], Google-Landmark[35], and in-house collected RGB images of real
and synthetic rooms. We trained on the entire dataset for first 600k
iterations, then we transferred the weights from first session, and trained on
only the indoor scenes for another 300k iterations.

In addition to retraining the neural network, we also apply the featuremap
refinement approach described in [20]. We use the multi-scale loss of [20],
and add a color histogram loss [2] between the histogram of the unmasked
pixels and the histogram of the inpainted image at each scale. This histogram
loss helps in getting rid of the gray areas while inpainting very large holes
in relatively homogeneous images (Figure 5).

These two modifications significantly refine the quality of the infilled
texture, when compared to directly using off-the-shelf model of [32].

Figure 5: Two examples of feature refinement. For each row, left-most image is
the masked input. Middle image is the prediction using [32] trained on our
dataset. Rightmost image is feature-refinement applied to the output shown in
the middle image. The carpet structure and the wooden textures are greatly
improved.

###  2.5 Inpainting non-planar areas

Since we are inpainting in a per-plane fashion, some pixels, which aren’t a
part of any plane, will be left out. To inpaint these regions, we first
replace all the pixels belonging to planar masked regions with their inpainted
values. This image along with the remaining mask is input to the inpainting
network. The output to this forward pass is the final inpainted image (Figure
6).

Figure 6: Inpainting of non-planar regions. The planar regions are inpainted
first, and then we do a final forward pass to inpaint the remaining masked
areas (observe the ceiling fan and ceiling light are inpainted in the bottom
image).

##  3 Evaluation

In this section, we discuss how we prepare the evaluation testset, followed by
the metrics we chose. We perform an ablation study to identify the
contributions of each component in our system.

###  3.1 Test Set

To evaluate our system, we collect a set of 229 captures of empty room scenes.
For each scene, we prepare inpainting mask by using silhouettes of virtually
placed furniture. The ground truth is set to be the original unmasked image.
The inpainting system is then tasked to inpaint this missing region. A
limitation of this method, as mentioned in [13], is that virtual masks in
empty room images do not cast shadows, and do not change the lighting of the
room by blocking a light source such as a window or a lamp. Despite that, it’s
a useful test-set to evaluate the inpainting performance, and perhaps the best
ground-truth one could get for natural indoor scenes.

###  3.2 Metrics

Earlier literature used to evaluate the inpainting performance on pixelwise
metrics, like PSNR, or mean squared error [26, 23], but these metrics aren’t
suitable to evaluate the performance on large mask inpainting, where there can
be a variety of visually distinct, but plausible solutions. Recent work[32,
11] has shifted to perceptual metrics, like Learnt Perceptual Similarity Score
(LPIPS)[44] and FID[14]. Our set of scenes is just 229, so we do not calculate
FID score because on a very small dataset, its estimated value significantly
differs from its true value[9]. For these reasons, our primary metric of
comparison will be LPIPS. Despite its limitations, we also report PSNR for
completeness.

In addition to this, we found that the visual experience of an empty room
substantially deteriorates if the inpainting introduces discontinuities or
edges. To quantify this, we introduce a new metric - incoherence. To calculate
incoherence, we first extract the edge-probability map [37, 27] for both the
ground-truth and the predicted image. All the pixels in predicted image, for
which the corresponding pixel is an edge in the ground-truth image, are
suppressed to $0$. Incoherence is then the average of edge probabilities
across all the pixels in the predicted image. A higher incoherence can
therefore be associated with more, or stronger, false edges in the inpainting.
The pseudo-code for this metric is given in Figure 7.

⬇

\pardef calc_incoherence(image_gt, image_pred, inpaint_mask):

edge_gt = gaussian_blur(edgemap(image_gt))

edge_pred = edgemap(image_pred)

# enhance all the edges above 0.1 probability

edge_gt[edge_gt>0.1] = 1.0

imask = edge_pred - edge_gt

imask[imask <= 0.01] = 0.0

incoherence = np.mean(imask[inpaint_mask])

return incoherence

\par

Figure 7: Python pseudocode for calculating incoherence. Figure 8: Incoherence
visualization. From left to right: ground-truth image, inpainted image,
incoherence map. The green/black colors are for unmasked/masked regions, and
the white color shows the incoherence introduced by the inpainting.

###  3.3 Results and Discussion

We discuss the results of our evaluation, which are summarized in Table 1.
Starting from the top row, we report the performance of an implementation of
PatchMatch [6, 24, 3] applied in a per-plane manner. Qualitatively, the
inpainting isn’t bad (row four of Figure 9), but there are many artifacts,
like texture and structural discontinuities. We next test LaMa [32] running
directly on the input image without any modifications. Numerically, it
performs better than PatchMatch, but the inpainting has poor geometric
consistency (row five of Figure 9). We add the geometric constraint to [32] by
performing inpainting using the Deep Neural Network, in a per-plane fashion.
It substantially improves the performance across all the metrics, and the
results look much better (row six of Figure 9). However, there is some blurry
gray infill in some places, like the left wall in second example, or the floor
in fourth example. Our texture refinement step, shown in the last row in the
figure, further improves the inpainting, and we are able to get rid of the
gray areas completely.

| LPIPS$\downarrow$ | Incoherence$\downarrow$ | PSNR$\uparrow$
---|---|---|---
PatchMatch[6]+P | 0.4329 | 0.0203 | 17.001
LaMa[32] | 0.3715 | 0.0121 | 20.033
LaMa+P | 0.3115 | 0.0063 | 20.689
LaMa+P+R (ours) | 0.2858 | 0.0036 | 21.141
Table 1: Ablation study of the texture infill on indoor scenes from our in-
house testset, using various methods. The abbreviations stand for: P-plane-
wise infill, R-texture refinement Figure 9: Qualitative examples from the
test-set. Each column shows a single example over multiple methods. The top
three rows are ground-truth image, masked input image, and the detected
layout, respectively. From the fourth to seventh row, we show the prediction
of: PatchMatch+P [6], LaMa[32], LaMa+P, LaMa+P+R (ours). Abbreviations are
described in Table 1.

##  4 Demonstration

We demonstrate how we can leverage our system to create a viable furniture
eraser application. The application starts with identifying all selectable
objects with a highlighted outline. Clicking on an object erases it from the
scene and uses the inpainted depth so that the user can place virtual
furniture in its place. Fig. 10 shows two examples of rooms being edited by
users.

Figure 10: Example of a furniture eraser application on two scenes. For the
top row, from left to right, we have input image, image showing furniture
highlighted as selectables, and an image showing a real furniture replaced
with a virtual one. The second row, from left to right, shows the room emptied
using our method, emptied room with one virtual furniture, and emptied room
with many virtual furniture items. The next two rows are same sequence, but
for another scene.

##  5 Conclusion

We proposed a modular system for removing furniture from an indoor scene by
leveraging perceptual cues such as segmentation and room layout. Our system
performs per-plane inpainting using a deep inpainting network, and we show how
removing any of the two components significantly degrades the inpainting
performance. In the last section, we demonstrated the application of our
system in re-decorating indoor spaces.

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