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: The paper proposes SageMix, a data augmentation technique for point clouds. Similar to the Mixup family of data augmentations, SageMix mixes two point clouds. It tries to mix point clouds in a saliency-guided way to preserve the salient local structures. Experiments have been conducted to show the efficacy of the method.
strengths_and_weaknesses: Strengths:

- The paper is well-written and easy to follow.
- It is nice that this data augmentation can lead to more robust networks as shown in Table 3. 

Weakness:

- A critical weakness is that experimental findings do not appear to be very significant. The performance difference between various techniques is not very large and no error margin has been reported (Table 2). It would be nice if the paper could provide some mean +- std measures. This could be done by running the same experiments multiple times (with random initialization) and reporting the mean and variance. This is particularly important as point-based benchmark methods can have significant variations across runs.

- Most experiments in the paper are limited to point cloud classification. The experiment on part-segmentation has not been described in detail. It would be nice to see experiments similar to Table 2 for segmentation as well. This would help in showing that the technique can be used beyond classification.
questions: Refer to the weakness section for questions. Overall, I am ambivalent about the paper. The method could be useful but I am unable to conclude from the experiments because of reasons mentioned in the weakness sections. Hence, I suggest a borderline rating for now. I will update the score based on the rebuttal.
limitations: NA
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper presents a method for the data augmentation of 3D point clouds. The proposed Saliency-Guided Mixup for point clouds (SageMix) preserves discriminative local structures and generates continuous samples with smoothly varying mixing ratios. Here saliency is computed based on the impact to the corresponding task, measured through the gradients of the loss. Experimental results show that SageMix brings consistent and significant improvements over state-of-the-art Mixup methods.
strengths_and_weaknesses: Strengths:
1. The saliency-guided sequential sampling is technically novel.
2. There are some ablation studies to demonstrate the effect of the proposed method.
3. Overall, the paper is well organised.

Weaknesses:
1. The difference between 2D and 3D mixup-based methods is not insightfully analysed in the introduction.
2. The shape-preserving continuous mixup component just follows the mainstream method and thus the novelty is limited.
3. The experimental results is not extensive. The proposed method is only quantitatively compared with PointMixup and RSMix. And there is also a lack of qualitative results.
questions: 1. Why 3D mixup is more challenging than 2D mixup? What is the particular difficulty of extending mixup from 2D to 3D?
2. The computation of saliency is similar to the popular CAM approach. What is the impact of using different methods for saliency compuation?
3. I suggest the authors make a more solid evaluation for the proposed method. 
limitations: n/a
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a novel saliency guided mixup method for point clouds. It first utilizes saliency to find a query point for each of the two point clouds. Then it uses an RBF kernel (around the query point) to calculate the blending weights for each of the points. This method generalizes PointMixup [2], and shows superior performance against existing ones.
strengths_and_weaknesses: Strengths
- The idea of introducing saliency guidance to the mixup method in point clouds sounds reasonable, as it has been proven to be useful in image-based methods [11] [12] [26].
- The design of sequentially sample two remotely located query (salient) points is delicate. It avoids the overlapping problem while preserving the important local structure of the two point clouds.
- The experiments seem sufficient.

Weaknesses
- I didn’t see any major weaknesses in this paper. One potential improvement to the method could be rotating and translating the whole point clouds to move the two query points far away from each other. In my opinion, it’s a more elegant way. Nevertheless, I think it’s fine to leave it as a future work.
questions: - About the robustness experiments in Table3. Why don’t the authors present all tests presented in Table & in RSMix [15] ? e.g. Rotation 90, Rotation Y, Scale 1.4 are missing.
- The authors mentioned their varying performance with different $\sigma$ in Appendix D.2. However, they didn’t quantitatively experiment the performance except for a qualitative one in Figure 3. How does $\sigma$ affect the performance of SageMix?
- Another minor issue is missing reference of a paper on arxiv (non peer reviewed, but with decent citations). PointCutMix: Regularization Strategy for Point Cloud Classification. In ArXiv. https://arxiv.org/abs/2101.01461.
limitations: N/A
review_title: null

Meta-review: metareview: 
This paper studies the point cloud data mixup with the saliency guidance. The proposed SageMix focus on the mixup over the local regions to preserve salient structures which are more informative for downstream tasks. The whole paper is well organized with clear logic to follow. The proposed method is simple but effective. Moreover, there are solid experiments in various tasks, including object classification, parts segmentation and calibration, to comprehensively evaluate proposed methods. One of the major concerns is the limited improvements over the standard mixup (Reviewer VLSt) on PointNet++. And the discussion of 2D and 3D mixup can be enriched in the aspects of technical challenges and novelties (Reviewer YgrL). This paper includes five different tasks and four benchmarks in experimental studies that strongly address the third major concern in the limited evaluation of Reviewer YgrL, who, however, has not provided any feedback after the authors' rebuttal. Considering the overall contributions in methods and solid evaluation, this submission is slightly above the bar of acceptance.
confidence: Certain
Decision: Accept