Learning to Sample

Created by Oren Dovrat, Itai Lang and Shai Avidan from Tel-Aviv University.

Introduction

We propose a learned sampling approach for point clouds. Please see our arXiv tech report.

Processing large point clouds is a challenging task. Therefore, the data is often sampled to a size that can be processed more easily. The question is how to sample the data? A popular sampling technique is Farthest Point Sampling (FPS). However, FPS is agnostic to a downstream application (classification, retrieval, etc.). The underlying assumption seems to be that minimizing the farthest point distance, as done by FPS, is a good proxy to other objective functions. We show that it is better to learn how to sample. To do that, we propose a deep network to simplify 3D point clouds. The network, termed S-NET, takes a point cloud and produces a smaller point cloud that is optimized for a particular task. The simplified point cloud is not guaranteed to be a subset of the original point cloud. Therefore, we match it to a subset of the original points in a post-processing step. We contrast our approach with FPS by experimenting on two standard data sets and show significantly better results for a variety of applications.

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{dovrat2019learning_to_sample,
  author = {Dovrat, Oren and Lang, Itai and Avidan, Shai},
  title = {{Learning to Sample}},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages = {2760--2769},
  year = {2019}
}

Installation and usage

This project contains two sub-directories, each is a stand-alone project with it's own instructions. Please see classification/README.md and reconstruction/README.md.

License

This project is licensed under the terms of the MIT license (see LICENSE for details).

Selected projects that use "Learning to Sample"

• SampleNet: Differentiable Point Cloud Sampling by Lang et al. (CVPR 2020, oral). This work extends "Learning to Sample" and proposes a novel differentiable relaxation for point cloud sampling.
• Multi-Stage Point Completion Network with Critical Set Supervision by Zhang et al. (submitted to CAGD; Special Issue of GMP 2020). This work evaluates our learned sampling as a supervision signal for point cloud completion network.
• MOPS-Net: A Matrix Optimization-driven Network for Task-Oriented 3D Point Cloud Downsampling by Oian et al. (arXiv preprint). This work suggests an alternative network architecture for learned point cloud sampling. To train their network, the authors use our proposed losses for S-NET and ProgressiveNet.