Towards Full-to-Empty Room Generation with Structure-aware Feature
Encoding and Soft Semantic Region-adaptive Normalization
Vasileios Gkitsas, Nikolaos Zioulis, Vladimiros Sterzentsenko, Alexandros Doumanoglou
and Dimitrios Zarpalas
Centre for Research and Technology Hellas, Thessaloniki, Greece
Deep Learning, Omnidirectional Vision, Image-to-Image Translation, Depth Estimation.
The task of transforming a furnished room image into a background-only is extremely challenging since it
requires making large changes regarding the scene context while still preserving the overall layout and style.
In order to acquire photo-realistic and structural consistent background, existing deep learning methods either
employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and
leverage it later in a not fully differentiable semantic region-adaptive normalization module. To tackle these
drawbacks, we treat scene layout generation as a feature linear transformation problem and propose a sim-
ple yet effective adjusted fully differentiable soft semantic region-adaptive normalization module (softSEAN)
block. We showcase the applicability in diminished reality and depth estimation tasks, where our approach
besides the advantages of mitigating training complexity and non-differentiability issues, surpasses the com-
pared methods both quantitatively and qualitatively. Our softSEAN block can be used as a drop-in module for
existing discriminative and generative models.
In recent years, deep learning has witnessed an un-
precedented pace of improvement, most notably con-
cerning the generation of high-dimensional content.
Contemporary approaches that leverage generative
adversarial networks (Goodfellow et al., 2014) have
shown impressive achievements in generating realis-
tic images after sampling from distribution as well as
various applications including image inpainting and
image-to-image translation.
This paper focuses on translating fully-furnished
rooms into empty ones. Specifically, the task aims to
hallucinate the occluded regions of an input image,
thus after translation, yields an image from the same
distribution but with a different context. In addition,
the growing interest in AR/VR applications has in-
creased the need for assisting applications in improv-
ing the user’s experience. Concerning interior redeco-
ration applications, diminishing objects from a scene
is of paramount importance, a task that can be ap-
proached by explicitly translating the existing scene
to a background-only scene.
Moreover, 360
devices get popularized, with
multiple panorama datasets (Armeni et al., 2017;
Chang et al., 2017; Zheng et al., 2020a) being avail-
able to facilitate the contemporary deep-learning-
based methods. The wide field-of-view, provided by
cameras further motivates the development of
image synthesis approaches in the 360
domain since
it provides enough surrounding context information.
Concerning occluded areas generation, the sur-
rounding context must be rich to aid the genera-
tion process. In a sense, synthesizing occluded ar-
eas of a scene can be approached by image inpaint-
ing. Nonetheless, while image inpainting manages to
generate plausible images that adhere to the distribu-
tion of the target image, it neglects to preserve the
fidelity of the occluded structure. This demanding-
ness makes the task lean toward an image-to-image
translation problem. On the other hand, image-to-
image translation methods aim to translate an image
from one domain to another, with one of its applica-
tions being the transformation of semantic labels to
real images. In that direction, owing to the need for
both preserving the structure of the concealed region
and the generation of plausible structures, (Gkitsas
et al., 2021) manage to approach the problem using
a hybrid approach. However, this approach heavily
depends on the necessity of a pre-trained dense lay-
out network to predict the three dominant semantic
classes of the scene(floor, wall, ceiling). This de-
mand not only makes it hard to re-train or fine-tune
the model on new datasets but also makes the train-
ing procedure complex and time-consuming. Further,
the style modulation derived from (Zhu et al., 2020)
Gkitsas, V., Zioulis, N., Sterzentsenko, V., Doumanoglou, A. and Zarpalas, D.
Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization.
DOI: 10.5220/0010833100003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
does not take into account the hole-filling nature in
the case of diminishing tasks. Specifically, the styles
used for modulating the layer activations are not se-
lected by considering the neighborhood of the hole
but rather explicitly derive styles from the whole im-
age. Finally, the single-stage approach is vulnerable
to artifacts, principally in challenging scenarios.
In this work, we investigate empty room gener-
ation from fully-furnished ones, as a probe to com-
prehend the capability of neural networks to generate
occluded regions of a scene. This is achieved by em-
ploying a two-stage, coarse-to-fine architecture. To
assist our network with the inferred scene structure,
we overcome the semantic segmentation network re-
quirement (Gkitsas et al., 2021) by exploiting the fea-
ture space of the coarse network and train our model
In summary, our contributions are:
We propose a full-to-empty room generation
model that learns simultaneously to infer the
scene dense layout, showing the benefits of end-
to-end training for both training simplicity and
model performance.
Using an adapted semantic region adaptive nor-
malization layer, we prove that we do not have
to resort to non-differentiable semantic maps to
modulate the layer activations.
Image-to-Image Translation: Image-to-image
translation approaches aim at translating a given
source image to a corresponding image of a target
domain. Over the last years, these approaches have
gained increased attention, due to their applicability
in a wide range of computer vision applications. Isola
et al.(Isola et al., 2017) first introduced the use of
conditional GANs for tasks as translating semantic
labels to images. In view of the recent works of
conditional adversarial networks, SPADE (Park et al.,
2019) introduces the spatially-adaptive normalization
layer in order to propel the semantic information
provided by semantic masks in the deeper layers of
the network. The modulation for the activations in
normalization layers is accomplished via a spatial
adaptive learned transformation. Accordingly, SEAN
residual block (Zhu et al., 2020) follows the same
path while tackling the two shortcomings of SPADE.
First, the use of only one style code to control the
style of the whole generated image, and second, the
absence of style contribution in the deeper layers
of the network. Both drawbacks are alleviated by
Figure 1: Modeling the full-to-empty room generation
task for spherical panoramas using our proposed approach.
From top to bottom: The input panorama, fully-furnished,
next the compared method (Gkitsas et al., 2021) generated
background panorama and following the one predicted from
our method. It is easily observable that our method pro-
duces more realistic results based on the ground truth empty
panorama. By making the semantic region-adaptive nor-
malization layer fully differentiable, the style modulation
can effectively retain the style of the scene background even
on extremely challenging cases.
incorporating for each semantic class its correspond-
ing style and thus using this style information via
spatially varying normalization parameters.
Multi-task Learning: Deep learning multi-task
methods aim at improving learned representation
via simultaneously utilizing multiple learning-based
tasks. Such approaches have been applied in several
applications (Liu et al., 2015; Jaderberg et al., 2016).
An important application in this context is semantic
segmentation. The incorporation of semantic segmen-
tation task has been studied to perform detection or in-
stance segmentation (Gidaris and Komodakis, 2015;
Chen et al., 2015; Pinheiro et al., 2016). Recently
introduced, (Xu and Zheng, 2021), employs multi-
task learning by leveraging the capability of gener-
ative networks to encode image semantics in its inter-
nal feature maps. Using a pre-trained GAN for gen-
erating an image from a latent vector, a simple lin-
ear transformation in the feature space is sufficient to
provide the semantic segmentation map. To supervise
the model, the ground truth semantic mask is obtained
by a pre-trained semantic segmentation network while
the standard cross-entropy loss is used as a loss func-
Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization
Image Inpainting: Traditional image inpainting
methods fill the missing content by either searching
the most similar patches in the background (Barnes
et al., 2009) or propagating neighboring structures
(Sun et al., 2005). However, these approaches strug-
gle when large regions are to be filled or content is
unique and not present in the rest of the image. On the
other hand, modern approaches (Iizuka et al., 2017;
Yu et al., 2018; Yu et al., 2019) leverage the recent ad-
vantages in deep learning to fill the missing regions by
learning from a large corpus of data. More recently,
some approaches assist the generation process by first
estimating structural information such as edges (Naz-
eri et al., 2019) and edge-preserved smooth struc-
tures(Ren et al., 2019).
Diminished Reality: Diminished Reality (DR) is the
process of removing objects that are perceivable in
our visual system. In order to diminish an object
from a perceived view, background information is re-
quired. This prerequisites for the viewer to observe
the occluded region from a different viewpoint or
in advance (Mori et al., 2017). Nonetheless, this is
not feasible in cases where real-time requirements or
technical substantial inability occur. In those cases,
the occluded areas can be inferred by image inpaint-
ing approaches. (Gkitsas et al., 2021) introduced a
hybrid image inpainting, and image-to-image trans-
lation method to approach the DR problem. More
specifically, first, the dense layout of the scene was
inferred by a pre-trained semantic segmentation net-
work. Next, the occluded regions were synthesized by
a single-stage generator. Additionally, SEAN residual
blocks (Zhu et al., 2020) were employed to modulate
the normalized activations using the dense layout and
the style codes from the input image.
In this section we present our approach, which is de-
picted in Fig. 2 for modeling the translation from full
to empty rooms.
First, we introduce the architecture of our gener-
ator, which is composed of a coarse and a refine net-
work. Second, we propose a method for inferring the
scene dense layout mask by leveraging the coarse net-
work features. Next, we present an approach for over-
coming the demand for a non-differentiable semantic
mask as input in the SEAN residual block. Last but
not least, regarding the diminishing task, we present a
simple yet efficient method for enforcing styles close
to holes to dominate in the generated occluded areas.
3.1 Coarse Network
Apart from the challenges posed by the need for hallu-
cinating occluded areas, inferring such a region with-
out first obtaining a coarse estimation is a challenging
task. Albeit there exist one stage approaches for im-
age inpainting (Zheng et al., 2019; Li et al., 2020) and
diminished reality (Gkitsas et al., 2021), the quality of
the generated content is prone to generate artifacts, es-
pecially when large holes occur. To mitigate this am-
biguity we follow a two-stage coarse-to-fine architec-
ture for our generator. Firstly, the input image, of size
256 × 512, is down-sampled to resolution 128 × 256.
Furthermore, we follow a slim architecture to reduce
the parameters. The scope of the coarse network is
to produce a coarse prediction, I
, that will be fed as
input to the refine network in order to facilitate the
hallucination of the occluded areas. In addition, the
dense layout map of the scene M
is generated from
the feature space of the decoder. Formally, given an
input, furnished image, I
, with foreground objects
to be removed, masked, we desire to learn a mapping
for the coarse network, G
, such that
} = G
3.2 Linear Transformation on Coarse
Network Feature Space
To provide the dense layout map of the scene, M
in the fine stage without necessitating a pre-trained
model (Gkitsas et al., 2021), we follow a nuanced ap-
proach. More specifically, assuming the coarse gener-
ator G
is composed of an encoder E
and a decoder
, then D
, in the i
layer, comprises x
feature maps. We denote with h
, the height,
width and channels of the i
layer, respectively. Ad-
ditionally, we denote as X IR
the upsampled
feature maps, to the output image resolution, of the
, concatenated along the depth axis.
Given an input furnished image I
to E
, we seek
to estimate a coarse prediction of the occluded back-
ground I
, alongside the dense layout of the scene.
Our intuition for obtaining the latter stems from the
hypothesis that the feature maps X encode the se-
mantics of the three abundant classes of an indoor
scene(ceiling, wall, floor) and can be inferred by ap-
plying a linear transformation on X .
Therefore, we aim to learn a mapping F such that:
= F(X )
Inspired by (Xu and Zheng, 2021), this mapping F
can be a linear transformation, defined as:
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Figure 2: The architecture of the proposed method, along with the flow of data, supervision, and losses used in each stage of
the end-to-end train. First, the input panorama is masked in the foreground area yielding I
and fed to the coarse network.
Next, after predicting the coarse background image I
, the dense layout of the scene M
is estimated by first applying
upsampling and concatenation in its decoder feature space and afterward applying a linear transformation. Following, I
composited with I
and fed to the refine network. After obtaining the encoded representation from the surrounding context
encoder, Ms is used along with style code obtained from I
for modulating the structure-aware decoder activations via
SSEAN block. The generated background image, I
is supervised by employing a low-level loss, a high-level loss as well an
adversarial loss. The layout estimation is supervised by the standard cross-entropy loss. The architectures of the discriminator
and SSEAN block are omitted for brevity.
) = T · X
where u
is the upsampling operation for the i
and T IR
3.3 Refine Network
After obtaining the coarse estimation I
, we seek to
eliminate generated artifacts from the coarse output
via the refine stage. The architecture of the refine net-
work G
is derived from (Gkitsas et al., 2021). In or-
der to exploit the obtained M
from the coarse stage,
we modify the blocks that leverage the dense layout
map for modulating the normalized activations of the
3.4 Soft Semantic Region-adaptive
In the recently introduced semantic region-adaptive
normalization (SEAN) layer (Zhu et al., 2020), the
generation process is assisted by conditioning its out-
put on one style code per semantic region. The origi-
nal formulation in (Zhu et al., 2020) assumes one hot
encoding for the semantic labels maps, which is typi-
cal in ground-truth annotated datasets. However, this
Figure 3: Our Soft Semantic Region-Adaptive Normaliza-
tion module which is built upon SEAN. First, the soft lay-
out semantic mask is applied with a weighted sum on the
encoded style, and afterward with a matrix multiplication
with the styles obtained for each semantic region. For more
info please refer to section 3.4.
demand hinders its applicability in end-to-end train-
able models, due to the fact that the operations in-
volved are not fully differentiable when the seman-
tic label maps are provided by a predictive model,
since in that case, the one-hot encoding is superseded
by a probability distribution among possible seman-
Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization
tic labels. In order to overcome this shortcoming, we
make two adjustments in (Zhu et al., 2020), the first
related to the style encoder and the second related to
the SEAN block.
In SEAN, the purpose of the style encoder is to
encode one style code per semantic label. This is
represented by a style matrix ST R
where C
denotes the total number of possible semantic labels
and D the dimensionality of the style code. ST is
computed via region-wise average pooling, from an
intermediate matrix
based on the se-
mantic segmentation map M R
, essentially
averaging the style codes at all spatial locations for a
given class. To make the process fully differentiable
in cases where a pixel belongs to a class with a given
probability, not necessarily strictly either zero or one
(the previously mentioned one-hot encoding assump-
tion), instead of using the one-hot encoded M, we em-
ploy M
. Let
i, j
denote the style code at the
spatial location (i, j) in
ST. Further, let p
i, j,c
[0, 1]
denote the probability that the pixel at spatial location
(i, j), belongs to class c as defined by the value of the
respective cell in M
. Then, the style code s
for the class c is computed by the weighted sum:
i, j
i, j,c
i, j
i, j,c
i, j
and the matrix ST is constructed by stacking {s
Regarding the SEAN block, we replace the broad-
casting operation that produces the stylemap SM
in a similar fashion. In particular, let s
denote the style code for class c which is com-
puted after the 1 × 1 convolution with the respective
style code in ST. In this context, the purpose of the
broadcast operation is to fill the stylemap’s spatial
locations at each pixel (i, j) with the corresponding
style code of the pixel’s semantic label. The original
broadcasting operation in SEAN does not take into
account probability distributions other than one-hot.
We make this operation soft and fully differentiable
by assigning to each pixel of the stylemap the sum of
all s
c weighted by the pixel’s probability to belong
to class c. More formally, let sm
i, j
denote the style
code in style map spatial location (i, j). Then:
i, j
i, j,c
In both previously mentioned modifications in or-
der to reduce the effect of mixing style codes belong-
ing to different semantic labels, we pre-process the se-
mantic label map M
(and consequently all p
i, j,c
) via
a sharpening operation powered by a softmax trans-
formation parameterized by sharpening constant K:
i, j,c
i, j,c
i, j,c
In our experiments we empirically set K = 0.1. To
that end, we polarize p
i, j,c
towards the extreme values
of 0 and 1.
With those two modifications, we make SEAN
fully differentiable and compatible with input seg-
mentation masks following arbitrary probability dis-
tributions across semantic labels. Other than that,
as depicted in Fig.3, we keep the rest of the SEAN
pipeline intact.
3.5 Supervision
In order to obtain the background image for a fur-
nished on, we combine several losses to obtain:
L = L
+ L
+ L
+ L
. (4)
A low level reconstruction loss L
, a high level syn-
thesis loss L
, an adaptive adversarial loss L
a layout estimation loss L
Low-level Reconstruction Loss. This pixel-based
loss focuses on the reconstruction of low frequency
components of the predicted image I
= λ
||vec(A |
||vec(A |
||vec(A |
where ||,|
| are the total number of pix-
els in
and in the respective gradient images in
x, y directions. A R
W ×H
is the spherical atten-
tion mask used in (Zioulis et al., 2019) that accounts
for equirectangular distortion, while the vec operator
treats its matrix argument as a flattened vector, and
|| · ||
, denotes the L1-norm. Finally, denotes the
Hadamard product.
Apart from the spherically weighted L1 loss, a to-
tal variation smoothness prior is used for the dimin-
ished area specifically to counter the high frequency
artifacts usually seen in the early training stages of
generative models.
High-level Synthesis Loss. Apart from encourag-
ing I
to have the same representation at the
pixel level with L
, we additionally employ a data-
driven loss L
. This enforces them to have a sim-
ilar representation in the feature space as computed
by a CNN model Φ, which in our case, is a pre-
trained VGG-19 (Simonyan and Zisserman, 2014).
Let Φ
(I) R
be the tensor of activations
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
of the j-th layer of the network Φ with C
for the given image I, and |
| the total number of
elements of the tensor.
Then the loss is formulated as a combination of
the perceptual and style losses:
= λ
+ λ
) Φ
)) G(Φ
are the perceptual and style (Gatys et al.,
2016; Johnson et al., 2016) losses, and G(M) =
,M R
is the Gram matrix function.
Both losses are derived in a high dimensional data-
driven feature space, with the former (perceptual) op-
erating on a global level, and the latter (style) operat-
ing on global and local levels.
Adaptive Adversarial Loss. To adaptively improve
the quality of the generated background images I
additionally employ a discriminator-based loss that is
learned during training. Since we use a PatchGAN
disciminator, we formulate our combined adversar-
ial loss as a combination of a hinge loss on the final
real/fake predictions (Lim and Ye, 2017), and a fea-
ture matching loss using the discriminator’s interme-
diate features:
= λ
+ λ
r(1 d
+ ||vec
r(1 + d
, (11)
where d
and d
are the discriminator outputs for the
real and predicted background images,
is the el-
ement domain of the discriminator’s output, |
| the
total count of its elements, while i denotes intermedi-
ate discriminator feature maps and |
| their spatial
element count. Finally, r stands for the ReLU acti-
vation. The spatial discriminator hinge loss and the
feature matching loss are weighted by their respec-
tive weights. Feature matching enforces the genera-
tor to minimize the statistical difference between the
features of the ground truth images and the generated
images, which helps further stabilize the training and
improve the quality of the generated content.
Layout Estimation Loss: To supervise the dense lay-
out estimation, we use the focal loss (Lin et al., 2017)
which is proven to penalize the network better than
the standard cross entropy loss on hard negative ex-
= a(1 p
) (12)
With p
and L
we denote the probabilities of the
target class and the standard cross entropy loss, re-
spectively. For our experiments we set α = 0.25 and
γ = 2.
Implementation Details. We implement our model
using PyTorch (Paszke et al., 2017) with all exper-
iments conducted on a Nvidia GeForce RTX 3090
GPU. Our generative models are optimized using
Adam (Kingma and Ba, 2014), with b
= 0.5 and
= 0.999, a learning rate of 0.0002 and a batch size
of 6. The input and output panorama resolutions are
256 × 128 for the coarse network and 512 × 256 for
the refine. The weights the models are initialized from
a zero-centered Normal distribution with σ = 0.02.
We empirically set λ
= 4.0, λ
= 1.0, λ
= 0.15,
= 40.0, λ
= 0.2 and λ
= 20.0.
Experiments. We compare our proposed method
against the set of the current state-of-the art meth-
ods of PanoDR (Gkitsas et al., 2021), RFR(Li et al.,
2020), PICNet (Zheng et al., 2019). Moreover, to
highlight the effectiveness of our method, we com-
pare it against PanoDR-e2e. PanoDR-e2e is consid-
ered as the official work, but trained end-to-end, us-
ing our adapted SEAN residual block. All training
configurations use the same adaptation of the Struc-
tured3D (Zheng et al., 2020b) dataset as in (Gkitsas
et al., 2021), with fixed seeds and using the official
train/test splits of (Zheng et al., 2020b). The men-
tioned adaptation enables the applicability of the orig-
inal dataset for diminished reality applications. For
more details please refer to the original work of (Gk-
itsas et al., 2021).
Quantitative Comparisons. Table 1 shows the
performance of each method on the standard set
of metrics, Mean Absolute Error (MAE), Peak to
Signal-to-Noise Ratio (PSNR), Structural Similarity
Index (SSIM), Fr
echet inception distance (FID), and
Learned Perceptual Image Patch Similarity (LPIPS),
with the three first expressing similarity in a spatially
local manner (pixel-wise or in small patches) and the
latter two in a perceptual-global manner. More pre-
cisely, LPIPS compares the features extracted by a
pre-trained VGG-16 model, rather than the images
themselves, with the rationale being that the extracted
features can be more expressive in local regions of the
image. On the same page, FID compares the high-
dimensional feature distributions of the predicted and
Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization
Figure 4: Qualitative comparison on diminished reality application from scenes in our test set. From top to bottom: Input
image with the diminished area masked with transparent red, PICNet, PanoDR, and ours.
ground-truth images, which assesses how close these
distributions are across the dataset. Given the nature
of our task, we aim to maximize the performance of
the perceptual metrics, since preserving the the struc-
ture of the room is more visually appealing, rather
than some minor photometric inconsistencies. Addi-
tionally, to assess the boundary preservation for the
generated image, we follow the mIoU estimation for
the introduced in (Gkitsas et al., 2021), by applying
a pre-trained semantic segmentation network on the
generated image and comparing it with the ground
Diminished Reality Application. Regarding the di-
minished reality application, given in Table 1, it is ap-
parent that our method not only surpasses the com-
pared methods in terms of perceptual metrics (LPIPS,
FID) but also exhibits equivalent performance con-
cerning boundary preservation. More specifically, the
model performance in terms of FID and LPIPS in-
creases by 2.1% and 16.6% over the baseline, respec-
tively whilst PSNR, SSIM, mIoU and MAE do not ex-
hibit significant variations. This performance gain is
attributed to the adapted SEAN residual block, which
manages to handle in a better manner the style modu-
lation of the scene.
Qualitative Comparisons. In order to further assess
the quality of the model performance, we take a closer
look at the qualitative comparisons. About the full-to-
empty room generation, it can be observed in Fig.1
that our method better preserves the overall hue of
the scene than the compared method. In such chal-
lenging scenes where the objects that are to be dimin-
ished cover a large part of the scene, it is crucial the
generated image preserves both structure and the hue
that depict the scene. For instance, in the first row
of Fig. 1 the floor of the scene is almost covered by
the bed and furniture of the room. PanoDR misses
generating floor with the realistic visual result, while
exhibits severe flaws with regard to the structure of
the scene. Similarly, in the second row, PanoDR not
only misses capturing the hue of the floor but also its
output is blurry at the lower side of the scene. On the
other hand, our method generates content with a hue
not vastly different than of the ground truth.
Concerning the diminishing application, in Fig.4
we compare our method with PICNet, RFR, and
PanoDR. The compared methods are prone to gen-
erating blurry images and in some cases, artifacts. In
contrast, our approach achieves visually appealing re-
sults given the challenging nature of that cases. For
example, one can see in the second column that the
texture of the generated image is compatible with that
of the surrounding context. In addition, albeit the ob-
ject to be diminished covers almost the 40% of the
scene, its output is free of artifacts and blurry regions.
Monocular Depth Estimation Task. To further eval-
uate the softSEAN block, we conduct another experi-
ment on a dense regression task, monocular depth es-
timation using the Structured3D dataset. We adapt
UNet architecture (Ronneberger et al., 2015) and de-
rive the direct supervision from (Zioulis et al., 2019).
Similarly, we employ a linear transformation, Lt to
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 1: Quantitative comparison on diminished reality application. Six metrics are used, FID, LPIPS, PSNR, SSIM, MAE
and structural preservation (mIoU) on the Structured3D test set( means lower is better, and means higher is better).
RFR (Li et al., 2020) 7.2474 0.0510 31.0114 0.9528 0.0067 0.8583
PICNet (Zheng et al., 2019) 6.7063 0.0533 32.3072 0.9557 0.0070 0.8502
PanoDR (Gkitsas et al., 2021) 6.8374 0.0398 33.6611 0.9620 0.0058 0.8768
PanoDR(e2e) 7.2052 0.0357 33.6681 0.9622 0.0060 0.8488
Ours 6.6915 0.0320 33.6576 0.9624 0.0058 0.8789
Figure 5: The architecture of the UNet adapted model us-
ing softSEAN block for depth estimation task. First, the
input image is fed to the model, next, a linear transforma-
tion is applied to obtain the soft semantic segmentation of
the scene, which is used to modulate the activation layers in
the decoder.
obtain the soft semantic mask of the input scene. Fol-
lowing a different line compared with the diminished
reality task, we use the features from the second layer
of the encoder to modulate the layers in the decoder.
The main goal is to enhance the predictions for local
regions through integrating the semantic map, com-
posed of 41 classes. The network architecture is de-
picted in Fig.5. Quantitative results are presented in
Table 2, for which we used typical metrics. The re-
sults enhance previous findings in that the softSEAN
residual block along with the exploitation of the lin-
ear transformation block enforces the network to bet-
ter predictions. Fig.6 clearly illustrates the benefits
of our method, in terms of both local and global re-
gions of the predicted depth map. For instance, in
the second row, the baseline model misses predicting
the correct depth due to texture transfer(highlighted
on the left) while fails at capturing local objects’
depth(highlighted on the right).
In this work, we propose an approach for indoor
spherical panoramas, in which an empty room is gen-
erated from a full-furnished one. The core idea of our
method lies in using a two-stage coarse-to-fine net-
work. First, a lightweight network is utilized to esti-
mate a coarse prediction of the background while en-
codes in its features and generating the dense layout
of the occluded regions of the scene. Subsequently, to
leverage the latter for modulating layers activations,
we adjust the SEAN block in a way that maintains
the differentiability of the dense layout. Interestingly,
we demonstrate that our method shows consistent im-
provement over the baselines regarding the dimin-
ished reality application while overcomes the barriers
of previous methods and is trainable in an end-to-end
manner. Further, we believe that the key insight of
this work can be applied to room re-decoration and in-
terior design applications. Last but not least, we vali-
date the effectiveness of the soft SEAN residual block
via applying it in the depth estimation task, showcas-
ing its efficacy for different computer vision tasks.
Table 2: Results of omnidirectional depth estimation. The
first row represents the baseline UNet architecture, the
second the adapted model with linear transformation (Lt)
and the latter the architecture that encompasses both linear
transformation and softSEAN.
Method RMSE RMSE(log) δ1 δ2 δ3
Baseline 0.4635 0.1738 0.9144 0.9613 0.9695
w Lt 0.4533 0.1684 0.9244 0.9675 0.9759
w Lt+so f tSEAN 0.3820 0.1585 0.9573 0.9768 0.9809
Figure 6: Qualitative results on omnidirectional dense depth
estimation on samples of Structured3D. From left to right:
input image, baseline, ours (Lt+softSEAN), ground truth.
This work was supported by the EC funded H2020
project ATLANTIS [GA 951900].
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Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization