@inproceedings{572598c22d944d9db712347865590c2c,
title = "Unsupervised Multi-view CNN for Salient View Selection of 3D Objects and Scenes",
abstract = "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.",
keywords = "Multi-view CNN, Unsupervised 3D deep learning, View selection, View-object consistency",
author = "Ran Song and Wei Zhang and Yitian Zhao and Yonghuai Liu",
note = "The paper was presented on 26th August 2020 and will be published in the proceedings of the conference, which the Lecture Notes in Computer Science. ; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
month = nov,
day = "13",
doi = "10.1007/978-3-030-58529-7_27",
language = "English",
isbn = "9783030585280",
series = "Lecture Notes in Computer Sciences (LNCS) - European Conference on Computer Vision",
publisher = "Springer Cham",
pages = "454--470",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",
}