Explaining the PointNet: What Has Been Learned Inside the PointNet?
Abstract
In this work, we focus on explaining the PointNet [4], the first deep learning framework to directly handle 3D point clouds. We raise two issues based on the nature of PointNet and give solutions. First, we visualize the activation of point functions to examine the issue how global features represent different classes? Then, we propose a derivative of PointNet, named C-PointNet, to generate the class-attentive responce maps to explore that based on what information in the point cloud is the PointNet making a decision? The experiments on ModelNet40 demonstrate the efficacy of our work for getting better understanding of PointNet
Cite
Text
Zhang et al. "Explaining the PointNet: What Has Been Learned Inside the PointNet?." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Zhang et al. "Explaining the PointNet: What Has Been Learned Inside the PointNet?." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/zhang2019cvprw-explaining/)BibTeX
@inproceedings{zhang2019cvprw-explaining,
title = {{Explaining the PointNet: What Has Been Learned Inside the PointNet?}},
author = {Zhang, Binbin and Huang, Shikun and Shen, Wen and Wei, Zhihua},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {71-74},
url = {https://mlanthology.org/cvprw/2019/zhang2019cvprw-explaining/}
}