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Unsupervised Detection of Distinctive Regions on 3D Shapes

Authors:

Daniel Cohen-Or,

Pheng-Ann HengAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 39, Issue 5

Article No.: 158, Pages 1 - 14

https://doi.org/10.1145/3366785

Published: 31 May 2020 Publication History

Abstract

This article presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct the analysis on point sets sampled from 3D shapes, then formulate and train a deep neural network for an unsupervised shape clustering task to learn local and global features for distinguishing shapes with respect to a given shape set. To drive the network to learn in an unsupervised manner, we design a clustering-based nonparametric softmax classifier with an iterative re-clustering of shapes, and an adapted contrastive loss for enhancing the feature embedding quality and stabilizing the learning process. By then, we encourage the network to learn the point distinctiveness on the input shapes. We extensively evaluate various aspects of our approach and present its applications for distinctiveness-guided shape retrieval, sampling, and view selection in 3D scenes.

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Cited By

Chen HZhao JChen KChen Y(2024)Self-supervised rotation-equivariant spherical vector network for learning canonical 3D point cloud orientationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107529128(107529)Online publication date: Feb-2024
https://doi.org/10.1016/j.engappai.2023.107529
Chen GDai HZhou TShen JShao L(2023)Automatic Schelling Point Detection From MeshesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314414329:6(2926-2939)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TVCG.2022.3144143
Song RZhang WZhao YLiu YRosin P(2023)3D Visual Saliency: An Independent Perceptual Measure or A Derivative of 2D Image Saliency?IEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3287356(1-17)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2023.3287356
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Index Terms

Unsupervised Detection of Distinctive Regions on 3D Shapes
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Information & Contributors

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 39, Issue 5

October 2020

184 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3403637

Editor:
Marc Alexa
TU Berlin, Germany

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Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 31 May 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 April 2020

Received: 01 July 2019

Published in TOG Volume 39, Issue 5

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Research Grants Council of the Hong Kong Special Administrative Region
Israel Science Foundation as part of the ISF-NSFC joint program

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Cited By

Chen HZhao JChen KChen Y(2024)Self-supervised rotation-equivariant spherical vector network for learning canonical 3D point cloud orientationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107529128(107529)Online publication date: Feb-2024
https://doi.org/10.1016/j.engappai.2023.107529
Chen GDai HZhou TShen JShao L(2023)Automatic Schelling Point Detection From MeshesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314414329:6(2926-2939)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TVCG.2022.3144143
Song RZhang WZhao YLiu YRosin P(2023)3D Visual Saliency: An Independent Perceptual Measure or A Derivative of 2D Image Saliency?IEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3287356(1-17)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2023.3287356
Liu XHan ZLee SCao YLiu Y(2023)D-Net: Learning for distinctive point clouds by self-attentive point searching and learnable feature fusionComputer Aided Geometric Design10.1016/j.cagd.2023.102206(102206)Online publication date: May-2023
https://doi.org/10.1016/j.cagd.2023.102206
Singer NAsari V(2022)View-Agnostic Point Cloud Generation for Occlusion Reduction in Aerial LidarRemote Sensing10.3390/rs1413295514:13(2955)Online publication date: 21-Jun-2022
https://doi.org/10.3390/rs14132955
Li XLi RChen GFu CCohen-Or DHeng P(2022)A Rotation-Invariant Framework for Deep Point Cloud AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309257028:12(4503-4514)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3092570
Singh VKambhamettu C(2022)AIM: an Auto-Augmenter for Images and Meshes2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00080(712-721)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00080
Chen XWu YXu WLi JDong HChen Y(2021)PointSCNet: Point Cloud Structure and Correlation Learning Based on Space-Filling Curve-Guided SamplingSymmetry10.3390/sym1401000814:1(8)Online publication date: 22-Dec-2021
https://doi.org/10.3390/sym14010008
Zhang MWang TCeylan DMitra N(2021)Dynamic neural garmentsACM Transactions on Graphics10.1145/3478513.348049740:6(1-15)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480497
Wang YLian Z(2021)DeepVecFontACM Transactions on Graphics10.1145/3478513.348048840:6(1-15)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480488
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