Paper: Point Neighborhood Embeddings (ICLR-2024)

Reviewer comment (Novelty):
The paper is very weak in novelty and makes some claims without evidence or explanations except results.

Author response:
**Weak novelty and not up to the level of ICLR**
>Novelty is a very subjective matter, and it can be difficult to rebut.
>However, we would like to point out that no other reviewer had this concern, and provided instead encouraging remarks such as "The paper provides a comprehensive and extremely insightful investigation about the point aggregation module.", "This paper fills a blank of this point convolution area", or "Their findings and recommendations can benefit future arch design of point clouds".
>We believe our work goes far beyond a simple review paper and provides several findings that can help move forward the field of learning on point clouds.

Reviewer comment (Evaluation):
The work mentions a lot of comparisons between activation functions, MLP vs. KP, etc. However, the whole paper lacks in explaining “why” something is better or worse. For example, kNN vs BQ talks about having high variance with kNN. It does not explain why.

Author response:
**kNN vs BQ talks about having high variance with kNN by does not explain "why"**
>Unfortunately, we were not able to communicate this properly.
>The reason why the high variance might hurt performance was described in the paragraph kNN vs BQ.
>Large variance will result in a large embedding norm for some MLP-based embeddings, ReLU and GELU, making training difficult. 
>Moreover, large variance will also result in some outlier points being not part of the support for some KP-based embeddings, *Gauss* and *Trian*, making them irrelevant for these computations.
>We have updated this paragraph to make the point clearer.

Reviewer comment (Novelty):
There is no fixed proposed architecture in the paper. The results are not generalized based on a particular setting. The architecture used is the existing architecture mentioned in 5.1 with some changes like activation function, using KP/MLP, and different correlation function based on KP embeddings.

Author response:
**There is no fixed proposed architecture in the paper.**
>Regretfully, we did not understand this specific concern of the reviewer.
> We believe the reviewer meant that our architecture is not original.
>However, this was not the aim of the paper.
>We aim to analyze and compare existing convolution designs from which we were able to gather new insights that help future designs of architectures for point clouds.

Reviewer comment (Experiments):
Why validation set is used for PNE and the validation+test set is used for other methods?

Author response:
**Why validation set is used for PNE and the validation+test set is used for other methods?**
>The labels for the test set of ScanNet are hidden and one is required to upload the predictions to their system.
>The system only allows uploading a set of predictions once every two weeks and only allows for a method per institution.
>Therefore, it was impossible for us to perform the PNE comparison on the test set.