Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network
Authors: Changfeng Ma
Yang Yang
Jie Guo
Fei Pan
Chongjun Wang
Yanwen Guo
Venue: NeurIPS 2022 Accept (2022)
Keywords: Point cloud completion
Point cloud segmentation
Unsupervised learning
GAN

Abstract: Most existing point cloud completion methods assume the input partial point cloud is clean, which is not practical in practice, and are Most existing point cloud completion methods assume the input partial point cloud is clean, which is not the case in practice, and are generally based on supervised learning. In this paper, we present an unsupervised generative adversarial autoencoding network, named UGAAN, which completes the partial point cloud contaminated by surroundings from real scenes and cutouts the object simultaneously, only using artificial CAD models as assistance. The generator of UGAAN learns to predict the complete point clouds on real data from both the discriminator and the autoencoding process of artificial data. The latent codes from generator are also fed to discriminator which makes encoder only extract object features rather than noises. We also devise a refiner for generating better complete cloud with a segmentation module to separate the object from background. We train our UGAAN with one real scene dataset and evaluate it with the other two. Extensive experiments and visualization demonstrate our superiority, generalization and robustness. Comparisons against the previous method show that our method achieves the state-of-the-art performance on unsupervised point cloud completion and segmentation on real data.
 

Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network
Changleng Ma Nanjing University
Yang Yang Nanjing University yyang hrraan.njuouutloo.coo com
Jie Guo
Nanjing University gnojie@nju.eem edu. cn
changf Saangfengma@smill njju. edu. cn
Fei Pan
Chongjun Wang Nanjing University chjwangenju. eduau. edu.
Yanwen Guo" Nanjing University yuguoEnju edu, cn
Nanjing University
panfei@sman eeeimmmmall...mmm nju. edu.
Abstract
Most existing point cloud completion methods assume the input partial point cloud is clean, which the case in practice, and generally based supervised learning. In this paper, we present an unsupervised generative adversarial au- toencoding network, named UGAAN, which completes the partial point cloud contaminated by surroundings from real scenes and cutouts the object simultane- ously, only using artificial CAD models assistance. The generator of UGAAN learns to predict the complete point clouds on real data from both the discriminator and the autoeneoding process of artificial data. The latent codes from generator are also fed to discriminator which makes encoder only extract object features rather than noises. We also devise a refiner for generating better complete cloud with segmentation module to separate the object from background. We train our UGAAN with one real scene dataset and evaluate with the other two. Ex- tensive experiments and visualization demonstrate our superiority, generalization and robustness. Comparisons against the previous method show that our method achieves the state-of-the-ee performance unsupervised point cloud completion and segmentation on real data.
Introduction
recent years, point clouds have gained more popularity 11 as the standard outputs of scanning devices [17 39] and the fundamental data structure to represent and process 3D data 7 6 19
However, clean and complete point clouds of objects, on which the downstream applications such reconstruction [ significantly rely, are hard to obtain in practice due to the nature of scanners and occlusions. Point cloud completion which infers a complete object model given a partial point
cloud thus has received considerable attention. Recent supervised methods [42 41
31
26
36 34
show remarkable performance on recovering the original shapes from incomplete point clouds, but generally assume that the input point cloud is clean and does not contain any noises and outliers. Such an assumption does not always hold in practice, because the surroundings around the target object will be inevitably seen by the cameras during scanning real scenes. Though semantic instance segmentation on point clouds has been investigated in the literature, the state-of-the-art instance segmentation methods [2 [5] only report around 68% average precision on the public ScanNet [7] and [I] datasets. That is to say, accurately cutting out the target object from the whole point cloud
*Corresponding author.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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still remains challenging. Therefore, it is important for the downstream applications to remove noises from point clouds or directly complete the point clouds with noises.
In this paper, we propose an unsupervised end-to-end network for completing the partial point cloud with noises from real scenes and outputting the object mask at the same time, dubbed UGAAN. Previous supervised point cloud completion and segmentation works require the paired clean and complete point clouds, together with object-level point labels of real scenes, for training, which are hard and costly to obtain in practice [39 22]. Most deep-learning-basee methods heavily rely on such training data, which limits their applications in practice. Clearly different from previous methods, our UGAAN only needs unlabeled real-scene objects and unpaired artificial CAD models for unsupervised training.
We employ the framework of GAN 10 which is widely used in unsupervised tasks. Traditional framework of GAN is hard converge due to the gap between real-scene data and artificial data. To resolve this problem, we utilize an autoencoding generator to learn the basic shapes of objects from the artificial data for easier convergence. Usually, the discriminator could easily distinguish the artificial data as real data and the prediction as fake data, leading to bad performance due to the data gap between predicted point clouds and artificial data. Our discriminator, by contrast, accepts the prediction of artificial data rather than the artificial data itself as real data, which can reduce the negative effect of data gap. Our discriminator also takes latent codes extracted from real-scene data and artificial data as the criterion, by which the generator can robustly learn better latent space for prediction. We employ refiner to refine and upsample the first prediction result, and the final complete point clouds thus generated are more accurate and uniform. Finally, the segmentation module predicts the labels according to the predicted point cloud and input point cloud. Compared with usual point cloud completion datasets [42) whose objects are aligned by the yaw angles and scales, real-scene datasets such as ScanNet and S3DIS 11 contain objects that have different yaw angles and varied scales, making the prediction of complete point clouds challenging. To accommodate real-scene data, we randomly rotate and scale the artificial point clouds for easier learning and convergence.
We train our UGAAN on ScanNet with ShapeNet as the artificial dataset without any pre-training or fine-tuning, and then directly evaluate it on the other two real-scene datasets, including S3DIS and ScanOhjectNN 28 Comparisons with the tate-of-the-aae methods 22 39 show the superiority of our method.
The main contributions of our work are as follows.
We propose, for the first time, an unsupervised end-to-end network for completion and segmentation of point clouds with outliers by combining GAN and an autoencoding network.
Our method completes the real-scene object with outliers from the background without the need of any pre- trainingg.
We conduct extensive experiments for analysis and comparisons on three different real-scene datasets, the results of which show the superiority and generalization ability of our methods.
The rest of this paper organized as follows. Section reviews briefly the works on point cloud completion and segmentation. Section 3 introduces our UGAAN in detail. Dataset generation, experiments and comparisons are shown in Section 4 and Section 5. Section concludes the whole paper and highlights future work.
2 Related Work
2.1 Point cloud completion and segmentation.
In the early stage, existing successful works 27 g utilize voxels as representations of 3D mod- els on which 3D convolutional neural networks can be immediately applied. PMP-Net [34 and PMP-Net++ [39] generate complete point clouds by moving input points to appropriate positions iteratively with minimum moving distance. SnowflakeNet (36) utilizes transformer and piie feature deconvolutional modules to refine the first-stage point could multiple times. For semantic segmentation, Jiang el al. [15] propose a multi-stage ordered convolution module to stack and encode the information from cight spaces, achieving orientation encoding and scale awareness. Liang et

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al. [2]] split nodes of pre-trained, intermediate, semantic superpoint tree for proposals of instance objects. Point Transformer [43] introduces transformer the encoding process of point cloud to learn the representation, which can be applied to different point cloud processing such as segmentation and classification.
2.2 Unsupervised point cloud learning.
Xiao et al. [37] summarize the unsupervised point cloud representation learning works using DNNs. 30 propose point cloud unsupervised pre-training approaches, Others proposed achieves excellent results on classification, segmentation and upsam- pling tasks. Jiang al. 14 introduce two contrastive losses respectively facilitate downstream classification and segmentation. Coseg [22) presents a point cloud object co-segmentation task, aiming to segment the common 3D objects in a set of point clouds, and fathom method with co-contrastive losses to minimize feature discrepancy inside estimated object points and maximize feature separation between the object and background points, which also leads to weak general- ization. Chen et al. 39) propose an unpaired point cloud completion method that can be trained without requiring explicit correspondence between partial and complete point clouds by employing an adaptive transform network in GAN, however, the training process of its network is complicated with several pre-training models.
Some works [20 38
unsupervised model 40
3 Method
3.1 Problem Formulation
Let P. E Mn, represent the point cloud from real-scene data with distribution p, and Pa denote artificial data with distribution Pa. respectively. P. contains points belonging to the object and from background (noises). (b)(P,) represents the object points on P,. Assuming that (b, ~ Po. the goal of unsupervised point cloud completion and segmentation is to train a generator G satisfying the following two conditions:
G(Pr;0g) ~ Pa-
2. G(Pr; 0 P, ~ Po.
where O,) represents the point cloud predicted by G with input P, and parameters 0g. and operation O denotes the intersection of two point cloud The two conditions are utilized to constrain completion and segmentation processes of point clouds, respectively. Condition 1 encourages the predicted shapes be similar to the shapes in the artificial dataset. Condition requires that the generator should predict shapes according to P, rather than generating random shapes, and the segmentation result which is the intersection of prediction and P. should match the object points.
3.2 Network Architecture
We employ Generative Adversarial Network (GAN) 10 to solve this unsupervised learning problem. As shown in Figure our UGAAN consists of autoencoding generator and discriminator that plays minimax game for unsupervised completion and segmentation of real-scene object point cloud. Furthermore, also utilize refiner refine the predicted complete point cloud and a segmentation module for label prediction.
the encoder E of the generator to extract the latent code z of input point cloud P., and multi-layer perceptron (MLP) as the decoder to predict the point cloud P. Mln, x3. The generator takes artificial data imput to learn basic shapes of point clouds. Then, we employ a lightweight encoder-decoder refiner 42 R on P. to get the refined and upsampled output Por Ming x3. Different from [42] the upsampling module directly refines the complete point cloud P.. Given an input point cloud P:. we can obtain the latent code, the complete prediction and the refinement by E(P::0)) (g) P. G(P:; and P. R(P, 0,), respectively, where 0, and 0, are the parameters of generate G and refiner It
Autoencoding Generator. We use PointNet++ 24
A point cloud P Minxa also be
containing points p E Mixx.

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UGAAN: Unsupervised Generative A Adversarial Autoencoding Network
taka real?
Encoder
Global
Decoder
Sequetiation Medule
Dramtshaae
real
Rellowr
Encoder
Latent-oode x
Decoder
Encoder
Global feature
MLP
P
Astommcoding Gonerulor
Diariniinator
Figure The overall architecture of our UGAAN. The network contains an autoencoding generator, discriminator, refiner and segmentation module. The autoencoding generator predicts the complete point cloud according to the input. The discriminator takes predicted point cloud and latent code as the criterion and decides whether the input point cloud is from artificial data or not. The refiner refines the complete point cloud, and the segmentation module predicts labels.
Discriminator: We take advantage of the encoder proposed in 42 to extract the global features f of P.. Then the global feature and the latent code z are combined and fed into MLP for predicting the probability P:
p( D(z, P.: = D(EP))))))
(1)
where P. Pa and 0a is the parameters of the discriminator. To reduce the negative effect of the shape gap between the prediction and artificial data, we feed the predicted shapes rather than the artificial data into the discriminator. The discriminator also takes latent codes as the criterion by which the generator can learn better latent codes for prediction.
Segmentation Module. Finally, the segmentation results (i.e. the object points of p,) are obtained under the guidance of the complete prediction Po. Here, we directly calculate the intersection between P. and P. to get the object points, rather than predicting with another network. Due to the discrete nature of point clouds, the strict intersection between P. and P. almost empty. As a consequence, we make use of general intersection between the point clouds P1 and P2 which is indicated by the overlapping area:
Pa P2 (cdp-in < <d Cd dlp Mix3).
where cdp->P, will be introduced in Equation with with tion label of p E P, conditioned on Po with threshold can be computed as:
being the threshold. The predicted segmenta-
Sa(p: P.,, Po) Po) {
1 p P P. Po. others,
(3)
Besides, the segmentation label of P, [pT D.. ,.., PT can be obtained by Sa(P,; P.) [Sa(Pr:Pr So)..,,,s((pp Pr, (o),...,(((p... Pr. Po)) E M,nx.
3.3
Optimization
We modify the optimization strategy introduced by GAN vised point cloud completion and segmentation problem. Specifically, the optimization problem of the proposed UGAAN is formulated as:
110 to train our UGAAN on the unsuper-
min max 84 P.-pa
E
[log P(P:; + EE P.Np.
[log (1 - P(P::dg
(4)
Due to the data gap between artificial data and real-scene data and the limited fitting ability of the decoder, the terms G(P,) and Ps often exhibit huge differences as shown in the top left part of FigureT] makes easier to optimize than G which makes network hard to converge and will result in poor completion performance. Thus, different from the original GAN where the

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discriminator directly works on G(P,) and Pas the discriminator of UGAAN predicts according G(P,) and G(Pa) to reduce the negative effects caused by these gaps for easier convergence. Besides, if we impose no other constraints problem 4 the generator may just generate random points after the optimization process complete. Thus, we also impose two losses on problem to to restrict the prediction of the generator, namely the autoencoding loss between (PPa) and Pa. and the reconstruction loss between ((P,) and P...
We employ the well-known Chamfer Dist ( to measure the similarity between point clouds. For point cloud P [pí .... Pj I where Py Mixx3 and a point p M the Chamfer Distance from to P is:
cdp min |p pylp?
(5)
j=l
For two point clouds Pi = [pf Pj .. 2 and P2, the Chamfer Distance from Pi to P2 is:
cdp,
1
Σ
cdp P2.
(6)
n1
Besides, the Chamfer Distance between P1 and P3 defined as:
cdp - + cdl:
(7)
The point cloud P1 will be a part of the point cloud P2 when the term cdp,-ps is minimized. Moreover, the point clouds P1 and P2 will be identical when the term edp is minimized.
We use the Chamfer Distance between G(P)) and Pa as the autoencoding loss. Beside, the Chamfer Distance from G(P,) to P. is utilized as the reconstruction loss, which makes the predicted shape be a part of the input and forces the generator to predict according to the input. We also apply the Chamfer Distance between R(G(Pa)) and Pa to optimize the refiner R.
In practice, we alternately optimize G with the optimization goal:
ming, Br
E
[ log P(P;; 0g 0a) + 02 clc(P.9)))P..+
Pewp.
(8)
E
(a3 cdC(P:00)) + cdR:c((P:)))..)))..))
PirPa
and with optimization goal:
min EE - as log (1 (1 P(P::; 0w))
+ E
a log P(P:: 0n)].
(9)
Ga Pimpp
PrrpP
where 01 are the weights used for balancing the influences between each term.
4 Experiments
4.1
Datasets
We first generate the complete point clouds by sampling CAD models of ShapeNet [3] and Mod- elNet [35], serving as the artificial data. We randomly rotate and scale them to simulate poses of real-scene data. Then we follow a similar process as ScanObjectNN 28 to generate the input point clouds with surrounding points (background points) by cutting out the points in axis-aligned boxes whose origins are the center objects in ScanNet 77 The sizes of these axis-aligned boxes are s larger than the axis-aligned bounding boxes of objects, where set 50%. We also record labels of points which are for object points and 0 for background points (noises) for evaluation. We select four categories to establish four datasets and randomly split the data into train, valid and test sets. The coordinates of all point clouds are normalized to [1,, Our UGAAN is trained on the train set, and evaluated test set.
4.2 Evaluation Metrics
Generating completion ground truth manually for real-scene data is hard. overcome this, we utilize Scan2CAD[2], dataset containing the alignment information from models of ShapeNet to scenes of
5

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Table 1: Comparison (mlOU) of segmentation results by our method (trained with train set of ScanNet) and previous methods on test sets of ScanNet and S3DIS.
Dataset
Setting
Model
Avg.
Bookcase
Chair
Sofa
Table
Full Sup.
PoinTTransformer
0.679
0.58
0.83
0.69
0.62
K- Mcans
0.201
0.25
0.23
0.18
0.16
ScanNet
Unsup.
SharinGAN
18
0.365
0.42
0.47
0.31
0.26
Unpaired
60
0,422
0.47
0.58
0.34
0.30
Ours
0.448
0.49
0.58
0.38
0.34
DGC
32
0.722
0.34
0.96
0.69
0.9
Full Sup.
PN++
24
0.735
0.44
0.92
0.75
0.83
K Means
0.252
0.36
0.18
0.27
0.2
S3DIS
AdaCoSeg
45
0.31
0.34
0.24
0,38
0.28
Unsup.
CoSeg
0.465
0.36
0.51
0.50
0.49
Unpaired
39
0,470
0.39
0.51
0.59
0.39
Ours
0.486
0.46
0.51
0.63
0.34
Table Comparison (CD) of completion results by our method and previous methods on test sets of ScanNet. The results are the smaller the better.
Model(CD 10.1%
Avg.
Chair
Table
Sofa
Bookcase
SharinGAN [8
0.0322 0.091
0.0376 0.095
0.0312/0.11 0.11
0.0224 0.111
0.0376 0.038
Unpaired 0.0405 O. 18188
0.0549 0.158
0.0339 0.274
0.0331
0.222
0.0403 100
Ours
0.0274/ / 0.217
0.0276 0.212
0.0279 0.300
0.0211 / 0.233
0.0330 0.125
to remove the unseen points caused by occlusion for simulating the real scene data. Then Chamfer Distance(CD) and F-Score@O. 1 (l.1%%FFFoc. (FF..1% score) [25 is employed to measure the difference between predicted complete point clouds and ground-tre -tuth models. For the evaluation of segmentation, we employ IOU, following measure the difference between prediction and ground truth.
ScanNet, to replace the incomplete objects with complete models, and use [66 23
the previous work [22]
4.3 Implementation details
Our framework is implemented using PyTorch. The point numbers Ro-2 in Section 3 are set to 2048, 512 and 2048, and the weights 01~6 are set to 100, 5, 100, 0.5 and 0.5. We train four categories separately with the Adam optimizer. The learning rate of the optimizer and the batch size for our network are set to 1.0 x 10-5 and 4, separately. We train the model for 240 epochs. Training our UGAAN takes about 20 hours for convergence with GTX 2080Ti GPU. We will make our dataset and codes public in the future.
4.4 Comparison
We compare our UGAAN against existing representative methods point cloud segmentation includ- ing unsupervised methods [22] 39
45 18 and supervised methods [43] 32 24) and on unsupervised
point cloud cloud completion [39 both quantitatively and qualitatively. We simply modify (39 by replacing its reconstruction loss with ours and retrain it to fit our datasets, and then utilize our segmentation module get the segmentation results for evaluation. As shown in Table we compare these methods on ScanNet and S3DIS with four categories, including the chair, table, sofa and bookshelf. Our method is trained on the training set of ScanNet and tested on the two datasets. The results are 6. 6616 and 4.51% % higher than [39] and [22). showing the superiority of our method. We also test our method trained with ScanNet with more categories
of these
the test set of ScanObjectNN, and the results are shown in the last second row of Table 3 There are only seven common categories in these two datasets. Thus, we can only conduct the comparison on these seven categories. We also train our UGAAN on 15 categories of ScanObjectNN together with ShapeNet and ModelNet to evaluate the generalization ability across multiple categories. The results are shown in the last row of Table 3 The results are 8. 737%% and 4.46% higher than [22] on "Avg. (part)" and "Avg. (all)". This verifies the generalization ability of our method with different categories. quantitative comparison of completion, we build test set using the method described in Section 4.2
the

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Table 3: Comparison (mIQU) of segmentation results by our and previous methods on test sets of ScanOhjectNN. The last two rows show the results of our mothed trained on train set of ScanNet and ScanObjectNN, separately. The "Avg. (part)" indicates the mIOU of the first seven categories (Bookcase Cabinet) and "Avg. (all)" indicates the mIOU on all the categories.
Setting
Model
Avg (port
Bookease
Chair
Sola
Table
Bed
Pillw
Cabinet
Bag
Bin
BoX
Dosk
Display
Done
Sink
Toslet
Arg. (all)
DGC
52
0.757
0.60
0.84
0.79
0.76
0.80
0.78
0.73
0.76
0.81
0.73
0.72
0.76
0.83
0.64
0.82
0.753
Foll Sup.
PN++
0.764
0.62
0.84
0.77
0.75
0.75
0.83
0.68
0.85
0.775
K-Means
0.370
0.34
0.40
0.41
0.31
0,38
0.37
0.38
0.43
0.42
0.41
0.38
0.38
0.45
0.39
0.40
0.389
AdaCoSeg
H5
0.367
0.26
0.55
0.54
0.23
0.37
0.29
0.33
0.38
0.31
0.48
0.37
0.44
0.35
0.43
0.44
0.385
Unsup.
CoSeg
22
0.561
0.48
0.58
0.64
0.44
0.64
0.60
0.55
0.66
0.70
0.68
0.46
0.62
0.74
0.57
0.68
0.605
Unpaired
30
0.450
0.40
0.43
0.47
0.41
0.49
0.50
0.47
Ours (ScunNet)
0.73
Ours (ScanObjectNNN
Scene
Input
Unpaired (Seg.)
Ours (Seg.)
Unpaired (Com.)
Ours (Com.)
Figure 2: Point cloud completion and segmentation results by Unparied [39] and our method. The categories from top to bottom: chair, table, and soda. Red and white in the inputs denote points on the partial objects and outliers, separately. The real inputs do not have the segmentation labels. The left columns shows the real-scenes containing input point clouds.
on the categories containing chair, table, bookcase and sofa. Table 2 shows that our UGAAN can predict more accurate complete point clouds.
Qualitative results on the three categories are shown in Figure 2 The point clouds produced by our UGAAN exhibit more accurate shapes and details, such as the legs and backrest of the chair and the legs of the table shown in Figure [ leading to more accurate segmentation. And the completion results of ours present more uniform spatial distributions, such as the sofa shown in Figure which contains less noise, which could be attributed to refiner. Uniform point distribution would facilitate further applications including re-construction, simplification, and so on.
4.5 Ablation study
Ablation study on network architecture. We conduct experiments on three ablated models to evaluate the necessity of important structures and reconstruction loss of our network. The first model is the basic structure of GAN with a feature extractor and shape generator. The generator predicts a complete point cloud according to the input point cloud, and the discriminator distinguishes between the prediction and the artificial object. The second model replaces the original generator with the autoencoding generator based on the first model. The third model adds the reconstruction loss introduced in Section 3.3 to the second model. The discriminator of the fourth model makes distinction between the predictions with real-scene data and the artificial object input separately. And the discrimination of our full model also takes latent codes as the criterion. As shown in Table 4
7

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we train and test these ablated models with the ScanNet dataset on the chair category. The evaluation shows the importance of these structures and reconstruction loss. The comparison against the third and fourth models shows that there is a gap between the predictions and artificial point clouds, which explains the reason we apply discriminator the predictions rather than the artificial point clouds directly.
Table 4: Ablation study of the proposed method.
Table 5: Ablation study of refiner.
Model
IOU
backbone
0.122
Dataset
ScanNet S3DIS ScanObjectNN
autoencoding generator
0,449
Ours (no refiner)
0.447
0.478
0.596
reconstruction loss
0.474
Ours (full)
0.444
0.486
0.610
discrimination on predictions
0,544
discrimination on latent codes (full)
0.580
Refiner. We also evaluate the importance of refiner both quantitatively and qualitatively. shown in Figure 3 the refiner can refine the prediction and make the prediction more uniform and reduce noises. The mIOU our methods without refiner and with refiner on the test sets the S3DIS and ScanObjNN are shown in Table 5 These methods are trained with the train set of ScanNet. The results show that even though the refiner takes no effect when method trained and evaluated on the same dataset, the refiner makes method more robust when evaluated new datasets.
Figure 3: The point clouds before and after refining on chair category.
The noise ratio s. We also set the noise ratio to 20%, 10%, 5% and 0%, and generate four more datasets with different noise ratios. Then we repeat the experiments above on the chair category and the IOUs are shown in Figure The lower the noise ratio is, the easier the problem is, making the model perform better. The results also show our generalization performance.
3.60
324
640
658
45
696
dale
pale
eai
62 + 5 55
usa
N4.00 Aalk
Theeee
Thrahes
Figure 4:
The IOU of our method on chair category with different noises ratio.
(a) Chair
(b) Bag
Figure 5: The IOU of our method with different threshold of inter- section on chair and bag categories.
The threshold of intersection. We also evaluate the influence of different threshold d of intersection on the IOU during the evaluation. As Figure shows, we apply different d to the categories chair and bag. The results show that different object has different best leading to best IOU according their volume in practice. Since we normalize the coordinates of point clouds to [-1, 1), the point clouds of small objects such as bags contain more details in the same space than big objects such chairs.
Extracted features of object points and background points. We visualize the extracted features of object points and background points from the encoder for analysis. As Figure (a) shows, every line indicates the feature of a point. The lighter the pixel is, the larger the value of this channel is. The figure shows that object points have larger values on some channels which can be passed the latent code by max-pooling operation. Figure b) shows the visualization of the features of artificial points clouds, whose pattern is similar to Figure a). since all the points of artificial data are object points.

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Visualization of latent codes. We use t-SNE [29] to visualize the latent code
distribution of real-scene data and artificial data. As shown in Figure 7 (c), the blue points represent the artificial data and red points indicate the real-scene data, and Figure 7 (e) shows the detailed point clouds of an area. The real-scene data that contains the same shape as the artificial data are grouped together, which shows the robustness of our method with input contaminated by noises. The point clouds having similar yaw angles are grouped together showing that our method could learn reasonable latent codes. This could be attributed to the fact that our discriminator also takes latent codes as the criterion. Visualization of the latent codes extracted from the method whose discriminator only takes predicted point cloud as the criterion is shown in Figure 7 (d). It is obviously observed that these two kinds of data are separated clearly. We also train our method with the dataset that mix up the real-scene data with different noise ratios. As shown in Figure 7 (a) and (b), similar object with different noise ratios are grouped together, which shows the robustness of our method on real-scene data with different noise ratios.
Nouss
225
205
(4) saaai---zzacccoo
Figure 6: Visualization of the extracted features real-scene data and artificial data. The bar on the left indicates the type (object or noise) of this line.
a
b
Figure 7: Visualization of t-E results with details. The same object with different noise ratio have the same color in Figure (b). Red and blue points are indicate the real-scene data and artificial data, respectively.
5 Conclusion, Discussion, and Future Work
We have presented UGAAN, an end-to-end network specifically designed for unsupervised completion and segmentation of point clouds contaminated by noises containing outliers. Benefiting from our proposed autoencoding generator and latent-code-based discriminator, our method able to generate complete point cloud accurately and segment the input real-scene point cloud in the meanwhile. Extensive experiments show the superiority and generalization of our method on different categories noises.
of different datasets. We also conduct experiments verifying the robustness of our method Our method needs artificial datasets during the unsupervised training, which limits our method to apply those categories without artificial correspondences. The encoder and decoder of our generator and the overall structure of GAN can be improved for hetter prediction and the segmentation module can be replaced with learning- module, which are the future work of our method.
Acknowledgments and Disclosure of Funding
This work was supported in part by the National Natural Science Foundation of China under Grant numbers 62032011, 61972194 194 and Natural Science Foundation of Jiangsu Province award numbers BK2021 1147.

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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Section S
(c) Did you discuss any potential negative societal impacts of your work? [No]
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [No] There is no theoretical results in this paper.
(b) Did you include complete proofs of all theoretical results? [No] There is no thcoretical results in this paper.
3. If you experiments...
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(a) Did include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material as URL)? [Yes] We include the pseudo code of our method in our supplementary material.
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 4.3
(c) Did you report error bars (c.g.. with respect to the random seed after running experi- ments multiple times)? [Yes] See our supplementary material.
(d) Did you include the total amount compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Section 4.3
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(a) If your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [Yes]
(c) Did you include any new assets either in the supplemental material or URL? [No] obtained from people whose data you're re
(d) Did you discuss whether and how consent
using/curating? [Yes] See the supplemental material.
(e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [Yes] See the supplemental material.
5. If you used crowdsoureing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, applicable? [No] There is no crowdsourcing or conducted research with human subjects.
(b) Did you deseribe any potential participant risks, with links Institutional Review Board (IRB) approvals, if applicable? [No] There is no crowdsourcing or conducted research with human subjects.
(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [No] There is no crowdsourcing conducted research with human subjects.
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Reviews:
[{"review_id": "cvRpD8fKN-j", "reviewer": "Reviewer_bh96", "summary": "This work proposes an unsupervised point cloud completion and segmentation method in the case that the input point cloud contains background clutters or noises. The core idea of the method is to train an autoencoder and a GAN at the same time between the corrupted real point cloud data to complete and segment, and the complete CAD model data. The authors conducted experiments over the ShapeNet, ScanNet, S3DIS, and ScanObjectNN data sets, and compared the proposed method to several previous works as baselines where superior segmentation performance is demonstrated. Extensive ablation studies are also presented to validate several network design details.", "questions": "I only have two questions below, but they are major concerns. I will consider raising up my score if the two questions are well addressed.\n- Can you show the setting where instance segmentators are used or justify why not do this experiment?\n- Can you add quantitative evaluation and comparisons regarding the shape completion performance?", "limitations": "The authors have discussed the limitations and I don't have further concerns.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- the proposed method contains several non-trivial technical designs, such as training the discriminators over the AE generated real and fake data, regularizing the GAN training with some reconstruction losses, feeding the latent codes to the discriminators as well, how to perform the segmentation, etc. The proposed method is valid, sound and innovative.\n- the authors conducted quite extensive experiments by using three popular scene datasets (ScanNet, ScanObjectNN, and S3DIS) and compared to several previous works as baselines, showing superior segmentation performance. The experiments are quite solid and convincing.\n\nWeaknesses:\n- the reviewer wants to question the assumption that only object bounding box information can be used to crop out the object with background clutters and noises. There are so many powerful instance segmentation methods, say Mask-RCNN and many 3D variants, which can crop out the foreground objects much better and cleaner than using only the bounding boxes as the cropper. While the reviewer agrees that these methods have failure cases, I believe that it's important to show experiments in these settings where the instance segmentation masks are used to crop out the objects and validate if the proposed method still demonstrates better results in these settings where the noises are much less and the data is much easier, maybe for competitive baseline methods such as [6]. I think many point cloud completion methods may assume using such instance segmentators to crop out the input object point clouds and also demonstrated experiments where certain noises are added to the input point clouds. Line 30-32 tries to argue that instance segmentation is hard, but please show it. I have big concerns regarding the input data assumption used in this paper.\n- Another big concern of mine is that though the paper claims in the title and the story that the method is doing well for completion there is actually no quantitative evaluation and comparison for the completion performance. The paper mostly evaluates regarding the segmentation performance. While the reviewer understands that it's impossible to get the ground-truth data for real-scene data sets, I still think there should be quantitative comparisons. At least the authors can show user studies results over a large scale of test data points. I think it's also helpful to use some synthetic data just to show some quantitative comparisons to baseline methods.\n- Table 1 and 2 does not include quantitative comparisons to [6], which is one of the most competitive method. The reviewer finds the comparisons in the supplementary material, but I think it's important to move them in the main paper.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "M32EkcYTZZ0", "reviewer": "Reviewer_ujzp", "summary": "This paper proposed a GAN to generate a complete point cloud from real scanned point clouds with the help of artificial CAD models. The completed point clouds are also used to segment objects from the background. Experimental results show the proposed method performs well in segmentation and demonstrates the proposed method can learn cross-domain information.", "questions": "Please address the above weaknesses. Besides, I think comparisons between the proposed method and the Unpaired are essential. I would suggest the authors move them into the paper.", "limitations": "The authors have not addressed the limitations or potential negative societal impact. My suggestions are listed in the weaknesses parts.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1.\tAchieves some kinds of unsupervised foreground-background point clouds segmentation.\n2.\tThe problem of noisy point cloud completion is interesting and valuable.\n3.\tMethod descriptions are clear and easy to understand.\n4.\tExperiments about segmentation and latent codes distribution are sufficient and informative\n5.\tThe overall architecture is reasonable and sound.\n\nWeaknesses:\n1.\tThe input of the mentioned segmentation requires the object located at the center. I think this requirement is too hard to fulfill in the real world, and the proposed method didn’t consider this.\n2.\tAlthough the paper is focusing on point clouds completion, statistics and comparison of completion performance are missing in the paper.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "7bMBo1yytP", "reviewer": "Reviewer_hDJC", "summary": "They propose UGAAN unsupervised end-to-end network for completing the partial point cloud with noises from real scenes. The motivation is that supervised point cloud completion and segmentation require the paired clean and complete point clouds, together with object-level point labels of real scenes, which is hard to obtain in real settings. They also propose using an autoencoding generator to learn the basic shapes from the artificial data, and  to help decrease the domain gap between real-scene data and make the optimization more stable. They show results on ScanNet with ShapeNet as the artificial dataset and evaluate it on the other two real-scene datasets, including S3DIS and ScanObjectNN.\n", "questions": "I think the method makes sense and has good results, but are few baselines which should be added such as SharinGAN which will make the paper much stronger.", "limitations": "Yes,  authors adequately addressed the limitations and potential negative societal impact of their work.", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1) The overall intuition behind the problem makes a lot of sense, collecting real world data for point cloud completion on large scale is a very challenging task and UGAAN can help in reducing the amount of required data for point cloud completion.\n2) The idea of an autoencoding generator is novel; their discriminator accepts the prediction of artificial data rather than the artificial data itself as real data, which helps in reducing the domain gap.\n3) They show decent results on ScanNet and transfer well to two real-scene datasets.  They also show ablation study for their method and for the refiner as well.\n\nWeakness:\n1) The idea of decreasing the domain gap between synthetic and real datasets is a separate field of research. For example, SharinGAN[1] also tries to decrease the domain gap. I think there should be comparison to more baselines, and at the very least there should be comparison to SharinGAN and how UGAAN performs wrt to SharinGAN[1].\n2) Also, the GAN used currently is the Vanilla GAN, there have been a lot of GAN’s that have come around which have much better performance than the Vanilla GAN. Instead of using Vanila GAN , maybe something like StyleGAN should be used which would probably give much better results.\n\n[1] SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry\nEstimation. https://openaccess.thecvf.com/content_CVPR_2020/papers/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.pdf", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept