Manifold Adversarial Learning for Cross-domain 3D Shape Representation

Hao Huang, Cheng Chen, Yi Fang

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


On a variety of 3D vision tasks, deep neural networks (DNNs) for point clouds have outperformed the conventional non-learning-based methods. However, generalization to out-of-distribution 3D point clouds remains challenging for DNNs. As annotating large-scale point clouds is prohibitively expensive or even impossible, strategies for generalizing DNN models to unseen domains of point clouds without access to those domains during training are urgently needed but have yet to be substantially investigated. In this paper, we design an adversarial learning scheme to learn point cloud representation on a seen source domain and then generalize the learned knowledge to an unseen target domain. Specifically, we unify several geometric transformations into a manifold-based framework under which a distance between transformations is well-defined. Measured by the distance, adversarial samples are mined to form intermediate domains and retained in an adaptive replay-based memory. We further provide theoretical justification for the intermediate domains to reduce the generalization error of the DNN models. Experimental results on synthetic-to-real datasets illustrate that our method outperforms existing 3D deep learning models for domain generalization.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031198083
StatePublished - 2022
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: Oct 23 2022Oct 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13686 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv


  • 3D point cloud
  • Adversarial learning
  • Domain generalization
  • Manifold and memory

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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