Unsupervised Multi-view CNN for Salient View Selection of 3D Objects and Scenes

Ran Song, Wei Zhang*, Yitian Zhao, Yonghuai Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

2 Citations (Scopus)


We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN for the salient view selection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of data collection. Our unsupervised multi-view CNN branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. We experimentally demonstrate the superiority of our method over state-of-the-art methods and showcase that it can be used to select salient views of 3D scenes containing multiple objects.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030585280
Publication statusPublished - 13 Nov 2020
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12364 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom


  • Multi-view CNN
  • Unsupervised 3D deep learning
  • View selection
  • View-object consistency


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