Beyond Farthest Point Sampling in Point-Wise Analysis release_kkjkk2lmtfdxbnuy5phyarbviy

by Yiqun Lin, Lichang Chen, Haibin Huang, Chongyang Ma, Xiaoguang Han, Shuguang Cui

Released as a article .

2021  

Abstract

Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling and downstream applications jointly. Our key insight is that uniform sampling methods like FPS are not always optimal for different tasks: sampling more points around boundary areas can make the point-wise classification easier for segmentation. Towards the end, we propose a novel sampler learning strategy that learns sampling point displacement supervised by task-related ground truth information and can be trained jointly with the underlying tasks. We further demonstrate our methods in various point-wise analysis architectures, including semantic part segmentation, point cloud completion, and keypoint detection. Our experiments show that jointly learning of the sampler and task brings remarkable improvement over previous baseline methods.
In text/plain format

Archived Files and Locations

application/pdf  6.2 MB
file_tfbfmthifjgc5l4doy6os6l5qm
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-07-09
Version   v1
Language   en ?
arXiv  2107.04291v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 6d03a9ac-acac-47d2-947a-dc686d4c4143
API URL: JSON