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: This paper tackles the problem of point cloud completion through using an incomplete point cloud and an auxiliary view-image as input. Moreover, this paper proposes using view-image as supervised signal and conducting self-supervised point cloud completion.
strengths_and_weaknesses: Strengths: 
(1) This paper achieves state-of-the-art results on point cloud completion. (2) Furthermore, it is the first proposing self-supervised setting for point cloud completion task, and I think it is the core contribution of this paper. Also, by only using the self-supervised setting, this paper achieves a comparable result with previous fully-supervised manners. (3) The writing for this paper is relatively straightforward and well-organized.

Weakness:
 (1) This paper is heavily based on the previous work ViPC. The core differences in the fully-supervised setting are only cross-modal fusion (attention), which is widely used for other multi-modalities tasks. 
(2) The Unimodal result in Table 3 is already higher than that of previous art (multi-modal), however, there is no clear illustration of why baseline performs pretty well. In addition, the performance improvement through introducing an auxiliary view-image seems marginal. 
(3) I think the self-supervised method in this paper seems overclaimed. Since auxiliary view-image is introduced in the supervision which contains partial information for the point cloud ground truth, it seems more like a weakly-supervised setting.
 (4) There is no ablation for the DCD loss.
questions: See the weakness.
limitations: The authors have not addressed the limitations and potential negative societal impact of this work.
review_title: null

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: This method proposes a corss-modal learning for image-guided point cloud shape completion. In this work, they employ a mixed strategy to train the network in a supervised and self-supervised manner. 
strengths_and_weaknesses: The image-guided framework seems to be an interesting setting for this problem.  On the other hand, the use of self-supervised stratgey also increases the generalization and robustness of this framework. 
questions: I have several conerns for this work:
1. In the introduction, their novel method is proposed to address one limitation in ViPC. However, the strategy used in ViPC is explicit, while the proposed method is implicit. Actually, once the fusion strategy happens in the feature domain, it is difficult to say what happens during the fusion process. If the authors want to say that their proposed method significantly outperforms ViPC, they should better clarify the limitations of ViPC.
2. In the checklist, the authors say that they have disccused the limitations in their manuscript, while it is actually just a hand-wavy sentence in the conclusion. I do not think this is a serious discussion for the limitation of this work. 
3. What is the main advantage of the proposed self-supervised loss. It seems that it is a very important component in this framework. However, I cannot find the main contribution of this module, considering the existence of the reconstruction loss. Besides, in the ablation study, the authors also say that this module just allows the training process to have a faster and smoother convergence. This description makes the module less important. Is is possible to provide more results like Figure 5? 
4. In the third column of Figure 4, the results obtained by XMFNet cannot outperforms PCN and VRCnet. 
limitations: I have shown all my concerns in above sections.  I have read the rebuttal and keep my original rating. Thanks.
review_title: null

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: The authors propose a new method for point cloud completion from the depth and RGB images. With the proposed cross attention and self-attention, the RGB and depth information can be better leveraged. Experimental results indicate that the proposed method outperforms the state-of-the-art methods. The self-supervised approach also outperforms a number of supervised methods.
strengths_and_weaknesses: **Strengths**

- The paper is clearly written.
- The experimental results are solid in both supervised learning and self-supervised learning.

**Weaknesses**

- The running time and model size comparison should be included.
- Most of the competitive methods only take the point cloud as input. Adding the RGB information will definitely improve the completion results. The proposed method outperforms ViPC significantly, but ViPC is not well compared to the other methods. PSGN (Fan et al., CVPR'17) should also be regarded as a simple baseline.
- Maybe it is harsh, but the manuscript does not bring new technical contributions.
questions: Please refer to the weaknesses.
limitations: In the Introduction, the authors claim that the application of the proposed method is to complete the missing shape of an object due to the wide use of RGBD cameras. However, the manuscript does not provide any experiments on real-world scenarios.
review_title: null

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: The paper proposes a point cloud completion approach with images as auxiliary information. The main challenge while utilizing the information from image for point cloud competition is effectively fusing local context between image and partial point cloud. This paper proposes using cross attention modules to fuse the local information from the image and point cloud. Furthermore, to supervised the neural network, two losses are used: 1) supervised loss based on chamfer distance between predicted and ground truth point cloud and 2) self-supervised silhouette reconstruction loss with the help of a differentiable rendering of predicted point cloud.
Experiments are done on ShapeNet-ViPC and the proposed approach outperforms the baselines.

strengths_and_weaknesses: **Strengths:**
1. The problem statement is clear, contributions are clearly highlighted and the method section is well written.
2. Fusion of local information between two modalities with the help of cross attention is a good idea considering that the point cloud is a partial representation of the original shape and direct one-to-one mapping between two modalities doesn't exist.
3. Interestingly the differentiable rendering of the silhouette gives rather a large improvement even though the silhouette is a rather weak signal.

**Weakness**
1. The main limitation of the work is its benchmarking, which is done on ShapeNet shapes. Though mentioned by the authors are the limitation, it still makes the paper rather weak because we don't know how well it works in complicated scenes (ScanNet).
2. The loss function is not fully ablated for example density aware Chamfer Distance.
3. Error bars are not shown in the experiments. I understand that the benchmark shows the best performing result, still, error bars can be shown in a separate table or in the Supplementary material.
4. Why do experiments in Table 1 only show 8 categories whereas the original dataset consists of 13 categories?
5. The mixup loss only provides a tiny improvement, is it worth including it in the training? How much computational overhead this does loss contribute to the training?


questions: 1. Explain M, N, and F in Figure 1. The figure and its captions should be self-contained. Try to have consistent notations in Eq 3,4 and the Figure 1.
2. What is \beta in the Eq. 8?
3. How are hyper parameters selected? Is there is a validation set?
4. To make a more convincing argument, the Figure 3 can be modified to show an average over several shapes.


limitations: I think more qualitative results indicating failure cases should be provided.
review_title: null

Meta-review: metareview: The paper proposes a point cloud completion method that can take an auxiliary image as guidance. All the reviewers rate the paper slightly above the bar. They like the reported strong performance over the prior baseline and also the capability of using the auxiliary input. Although several reviewers raise concerns about missing experiments on real datasets such as ScanNet or KITTI, they still think the paper has sufficient merit. The AC finds no strong reason to disagree with the reviewers.
confidence: Certain
Decision: Accept