weakness of each approach. - All the scores in Table 3 (Avg) is lower than their Table 2 counterpart, which makes me wonder if the imbalance nature of the data across categories has more effect than it should be. Chair and table sort of dominate the dataset, and skew the final score toward the trend of these two classes. I feel like a more fair comparison is when all class has an equal number of object or when each class is weighted equally. I know that this is pretty common in classification task, but it can be misleading. ? The result for bed is very interesting and worth a discussion. MCD [23] outperform other methods by a large margin. And if we assume that the proposed approach with only G is the same as MCD, then adding local alignment drop the classification score from 26.1 to 4.3 (and adding attention further drop them to 1) Do you have any intuition on why this is the case? Clarity: + Overall the paper is not difficult to understand, + The format of the experiments, ablation study, and the tables to show the results are all very clear and easy to digest. ? I feel like section 3.5 doesnât add much to the narrative and could be put in the supplementary instead. - Itâs not immediately clear to me in line 90-91 that P(s) P(t) refers to the distribution (itâs defined in the next section) Significant: + I believe the idea of self adaptive node for 3D object would be useful to the research community, if it works. Aligning feature might not be new, but doing so in 3D setting and on top of PointNet based feature shows that it is possible and promising, at least for chair and table categories. + Itâs true that not much works are looking into Domain Adaptation for 3D data, and it helps to have a common benchmark even if it just a combination of existing dataset. --UPDATED AFTER REBUTTAL-- Thanks for the detailed rebuttal. The additional results are quite interesting and further convince me that the proposed local alignment does help. So I'm keeping my score at 6.
- Itâs not immediately clear to me in line 90-91 that P(s) P(t) refers to the distribution (itâs defined in the next section) Significant: