RotPredictor: Unsupervised Canonical Viewpoint Learning for Point Cloud Classification

Jin Fang, Dingfu Zhou, Xibin Song, Shengze Jin, Ruigang Yang, Liangjun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Recently, significant progress has been achieved in analyzing the 3D point cloud with deep learning techniques. However, existing networks suffer from poor generalization and robustness to arbitrary rotations applied to the input point cloud. Different from traditional strategies that improve the rotation robustness with data augmentation or specifically designed spherical representation or harmonics-based kernels, we propose to rotate the point cloud into a canonical viewpoint for boosting the following downstream target task, e.g., object classification and part segmentation. Specifically, the canonical viewpoint is predicted by the network RotPredictor in an unsupervised way and the loss function is only built on the target task. Our RotPredictor satisfies the rotation equivariance property in (3) approximately and the predication output has the linear relationship with the applied rotation transformation. In addition, the RotPredictor is an independent plug and play module, which can be employed by any point-based deep learning framework without extra burden. Experimental results on the public model classification dataset ModelNet40 show the performance for all baselines can be boosted by integrating the proposed module. In addition, by adding our proposed module, we can achieve the state-of-the-art classification accuracy with 90.2% on the rotation-augmented ModelNet40 benchmark.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on 3D Vision, 3DV 2020
Pages987-996
Number of pages10
ISBN (Electronic)9781728181288
DOIs
StatePublished - Nov 2020
Event8th International Conference on 3D Vision, 3DV 2020 - Virtual, Fukuoka, Japan
Duration: Nov 25 2020Nov 28 2020

Publication series

NameProceedings - 2020 International Conference on 3D Vision, 3DV 2020

Conference

Conference8th International Conference on 3D Vision, 3DV 2020
Country/TerritoryJapan
CityVirtual, Fukuoka
Period11/25/2011/28/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • point cloud classification
  • rotation equivariance

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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