Venue: NeurIPS 2022 Conference

Reviews:

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: The authors propose to utilize the part-level feature representation for the cross-domain generalization problem in point cloud applications. Given part-level features grouped from point-wise representations, the authors first align them to a learned feature dictionary via cross-attention, and then aligned-features are aggreged with a part-weighted maxpooling strategy. In addition, contrastive learning is conducted in both shape-level and part-level. Empirical results on standard DG benchmark datasets are presented for validation. 
strengths_and_weaknesses: Strengths:
1. The method is well motivated.  The authors find that part-level features present smaller distribution divergence than shape-level feature in the cross-domain tasks. Therefore, they propose to adopt part level features in the DG tasks.

2. Some interesting components are proposed and well justified. The proposed part-template features implicitly achieves the domain alignment by aligning both domains to the learned feature dictionary. The proposed part feature aggregation module outperfoms the popular max pooling module. 

3. The proposed method achieves state-of-the-art performance on DG benchmarks. 

Weaknesses:
1. I am wondering the relationship between the proposed part feature based DG method and the general point cloud models (e.g., PointNet++) that utilize part/local features.   For example, in the PointNet++, the part level feature is extracted and aggregated hierarchically, which is quite similar to the strategy adopted in this paper.  Could you clarify it? 

2. Based on the first question, could the proposed module be adopted in general point cloud models? For example, could we replace the last max pooling layer of PointNet++ with the part feature aggregation module proposed in this paper? 

3. As for the proposed techniques, the contrastive loss is widely adopted as the learning regularization and utilizing part level features is also a common practice. In my opinion, the main contribution is the implicit domain alignment with the learned feature dictionary and the part feature aggregation module. So I suggest that the authors include more related work on the application of dictionary learning in cross-domain problems, such as [1]

[1] Li, Shuai, et al. "Category dictionary guided unsupervised domain adaptation for object detection." Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 3. 2021 
questions: See weaknesses.
limitations: Not found.
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: The authors present a new method for generalizing point cloud classification from synthetic to real data. The authors argue that the local geometric features are more generalizable than the whole shape. They focus on part-based feature representation, and design a part-based domain generalization network for the point cloud classification task. The key idea is to build a common feature space using a part template, and then align the part-level features of the source and the target domain to this template. The authors demonstrate that the proposed method achieves state-of-the-art performance on the 3DDG benchmark. 
strengths_and_weaknesses: - I like the idea to align local geometric features to solve domain generalization on point clouds. This idea is novel and significant. The technical approach to implement this idea is sound, and the experimental results demonstrate good performance.

- I also like the idea to verify the hypothesis that local geometric features are more generalizable than global features in Fig. 2. However, I would like to point out a few issues here. 
  (1) It is true that in general reducing the part size leads to better generalization. But where is the limit? At the very least each part can be reduced to a point, but I do not believe that point-based features are the most generalizable. It could be more interesting to identify by how many points per part we would reach to limit of generalization here. 
  (2) 512-part-level and 256-part-level mean 512 and 256 points per part, respectively I guess. This sounds confusing as I can also think of it as 512 parts and 256 parts. It is better to revise this wording, like 512-points-per-part and 256-points-per-part. 

- I also value the clarity of the writing, which is very nice and easy to read. 

- Despite its great values, the paper suffers from the following issues. 
  (1) In terms of technical approach, the contrastive learning part is less well connected to the part-based features for domain generalization. For example, if the authors wish to use contrastive learning, at least shape-level contrastive loss should be used for the baseline methods as well. Or the comparisons should be separated with a table with no contrastive learning utilized. In Table 1, as I understand, the baselines are without contrastive loss but the PDG is with contrastive loss. Please correct me if I misunderstood. 
  (2) My second concern is that the experiments conducted are somewhat simplistic. I expect deeper analysis and more experimental settings to be done. Please see my comments in the question section. 
questions: (1) For completeness, I expect to find some information about how the part template is defined. However, it seems not provided except that in Line 246 the authors mentioned that they followed the experiment setting in MetaSets [22]. Could you clarify how the parts are defined? How does this affect the final performance as the proposed method is largely based on part features? 

(2) While the benchmarks are built upon ModelNet/ShapeNet and ScanObjectNN, I see that not all data are utilized. For example, the experiments are only done on two basic variants of ScanObjectNN including OBJ-ONLY and OBJ-BG. How does the transfer work if the target is the hardest variant of the ScanObjectNN? 

(3) As a domain generalization problem, it seems insufficient to conduct only sim-to-real transfer experiment. How about real-to-sim, i.e., training on ScanObjectNN and testing on ModelNet/ShapeNet? In practice, this could be used to build applications such as retrieving a CAD model from a given scan. As all data is available, I wish to further understand what challenges remain that make this experiment not conducted in both MetaSets and this paper. 

limitations: I think the related work section in this paper is quite short and needs some revisions. First, while not exactly the same, I found in the literature there are some 3D tasks that link different domains together such as scans and CAD object retrieval. I think this is worth some further discussions about the connections of these specific tasks with the domain generalization problem presented in this paper. 
[A] SHREC'17: RGB-D to CAD retrieval with ObjectNN dataset, 2017, 2018.
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: The presented method detects “global” features (PointNet or DGCNN)  locally on sampled points. Then learn relations between those local representations as part-level aggregation. The performance is further improved by contrastive learning. 

Authors evaluate the approach on several cross domain datasets where the method is learnt on one domain and tested on another one. The target (test) domain is inaccessible during training.

Domain adaptation is an important and well old problem for 3d point cloud processing.

strengths_and_weaknesses: Pros:
-Clear motivation, I like the motivation by features distance between domains (Fig. 2)


-While the idea of learning part-based models from local features is old and highly researched, the presented method on 3D point clouds with Neural Networks focused for the domain adaptations seems novel.

-The problem of domain adaptation of 3d point cloud processing when the target domain is unavailable  during training is a very important and unsolved problem.

-Most of questions I had during reading were further answered


Cons:
-The approach is motivated from many previous works that focus on domain adaptation for 3d point cloud that are not cited, though the approach is novel by using it in neural networks.

-Authors did not evaluate on some datasets pairs (training-testing) that will allow much broader comparison. That raises several questions why. I believe it will be also good to report why presented numbers of other methods differ from original papers.

questions: -Why did authors not report M->S like a comparison to other methods? It would be a very nice comparison to other papers [Achituve21, Shen22,..]. It looks strange that evaluation is done with datasets [11],[12],[14], but popular [13] (ScanNet) is missing, ScanNet is often used in previous methods  [Achituve21, Shen22,..]. Readers can think about many reasons why authors didn't report it and it looks strange that authors perform too many results, but the most important settings are somehow missing.

-Authors say that they executed the competitors' code, still, they have different performance values than in other papers (in other papers they also vary from paper to paper).  E.g. M->S*, MetaSet paper claims they have 72% (so better than the presented method), but authors report ~66% as best for Metaset. Is it a problem of the field that once you download a repository of a paper you obtain different results that were originally published? The difference is higher than the paper’s improvement :( 


-If one looks on the proposed method as using "global" features (e.g. PointNet) locally on parts of the object and then learning relations of those local responses, then this is an old method how to represent 3d point cloud object in the scene between different domains, for example [Woodford13, Knopp10] learns spatial configuration of such features for the nice 3d shapes and apply it on the 3d scans, [Kim12] learns parts deformation of the 3d shapes and recognize them in the scanned cluttered scene. [Lai10] Looks like one of the first method to do 3d point domain adaption. Here is list of related missing work:
[Lai10] Lai and Fox, Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation, IJRR 2010
[Ishimtse20] Ishimtse at all, CAD-Deform: Deformable Fitting of CAD Models to 3D Scans, ECCV 2020
[Qi19] Qi et all, Deep Hough Voting for 3D Object Detection in Point Clouds, ICCV 2019
[Golovinskiy09] Golovinskiy et al, Shape-based Recognition of 3D Point Clouds in Urban Environments, ICCV'09
[Knopp10] Knopp et all; Hough Transform and 3D SURF for Robust Three Dimensional Classification, ECCV 2010
[Woodford13] Woodford et all, Demisting the Hough Transform for 3D Shape Recognition and Registration, IJSV 2013
[Kim12] Y.M. Kim et al., Acquiring 3D Indoor Environments with Variability and Repetition, ACM ToG, SIGGRAPH Asia 2012

limitations: It looks there is no potential for negative social impact of the work.
review_title: null

Meta-review: metareview: The paper works on domain generalization of 3D point cloud classification, and proposes a part-based domain generalization network for the purpose, whose key idea is to build a common feature space of part template and align the part-level features wherein. Three reviewers appreciate the contributions, including the clear motivation, the implicit domain alignment by part-template features, and the proposed part feature aggregation module. They also suggest to improve the paper by clearer definitions of parts, better organization of contrastive learning in the paper, a more complete citation of closely related works, etc. 

After discussions between the authors and reviewers, consensus is reached on accepting the paper.  Congratulations!

confidence: Certain
Decision: Accept