Statistical Convolution on Unordered Point Set

Anh Duc Nguyen, Seonghwa Choi, Woojae Kim, Sanghoon Lee, Weisi Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a new convolutional layer for neural networks on unordered and irregular point set. Most research advanced to date usually face multiple problem related to point cloud density and may require ad-hoc neural network architectures, which overlooks the huge treasure of architectures from computer vision or language processing. To mitigate these shortcomings, we process a point set at its distribution level by introducing statistical convolution (StatsConv). The spotlight feature of StatsConv is that it extracts various statistics to characterize the distribution of the input point set, which makes it highly scalable compared to existing point convolution operators. StatsConv is fundamentally simple, and can be used as a drop-in in any contemporary neural network architecture with negligible changes. Thorough experiments on point cloud classification and segmentation demonstrate the competence of StatsConv compared to the state of the art.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages3468-3472
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - 2020 Oct
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 2020 Sept 252020 Sept 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period20/9/2520/9/28

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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