Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: 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.

strengths_and_weaknesses: Strengths:
1) 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.
2) 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.
3) 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.

Weakness:
1) 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].
2) 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.

[1] SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry
Estimation. https://openaccess.thecvf.com/content_CVPR_2020/papers/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.pdf
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.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: 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.
strengths_and_weaknesses: Strengths:
1.	Achieves some kinds of unsupervised foreground-background point clouds segmentation.
2.	The problem of noisy point cloud completion is interesting and valuable.
3.	Method descriptions are clear and easy to understand.
4.	Experiments about segmentation and latent codes distribution are sufficient and informative
5.	The overall architecture is reasonable and sound.

Weaknesses:
1.	The 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.
2.	Although the paper is focusing on point clouds completion, statistics and comparison of completion performance are missing in the paper.

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.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: 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.
strengths_and_weaknesses: Strengths:
- 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.
- 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.

Weaknesses:
- 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.
- 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.
- 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.
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.
- Can you show the setting where instance segmentators are used or justify why not do this experiment?
- 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.
review_title: null

Meta-review: metareview: This paper proposes to leverage generative models (GAN) to perform cross-domain data generation for point cloud completion and segmentation applications. All three reviewers voted to accept the paper, citing novelty, good writing and convincing experimental results. Authors are requested to take into account reviewer feedback and incorporate into their final version. Congratulations!
confidence: Certain
Decision: Accept