research-article

Orientation invariant 3D object classification using hough transform based methods

Authors:

Luc Van GoolAuthors Info & Claims

3DOR '10: Proceedings of the ACM workshop on 3D object retrieval

Pages 15 - 20

https://doi.org/10.1145/1877808.1877813

Published: 25 October 2010 Publication History

Abstract

In comparison to the 2D case, object class recognition in 3D is a much less researched area. However, with the advent of affordable 3D acquisition technology and the growing popularity of 3D content, its relevance is steadily increasing. Just as in the 2D case, 3D data is often partial, noisy and without prior segmentation. Moreover, the object is rarely observed in a canonical frame of reference with respect to orientation (or scale). We propose a method, using Hough-voting for local 3D features, which is orientation (and scale) invariant.

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Cited By

Shu ZYu JChao KXin SLiu L(2025)A Multi-Modal Attention-Based Approach for Points of Interest Detection on 3D ShapesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336876731:3(1698-1712)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3368767
Min JKim SCho M(2023)Convolutional Hough Matching Networks for Robust and Efficient Visual CorrespondenceIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3233884(1-16)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2022.3233884
Min JCho M(2021)Convolutional Hough Matching Networks2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00296(2939-2949)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.00296
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Index Terms

Orientation invariant 3D object classification using hough transform based methods
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Shape modeling

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Published In

cover image ACM Conferences

3DOR '10: Proceedings of the ACM workshop on 3D object retrieval

October 2010

96 pages

ISBN:9781450301602

DOI:10.1145/1877808

Program Chairs:
Mohamed Doudi
TELECOM Lille1/LIFL, France
,
Michela Spagnuolo
CNR-IMATI, Italy
,
Remco Veltkamp
Utrecht University, The Netherlands

Copyright © 2010 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 ACM 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10: ACM Multimedia Conference

October 25, 2010

Firenze, Italy

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Cited By

Shu ZYu JChao KXin SLiu L(2025)A Multi-Modal Attention-Based Approach for Points of Interest Detection on 3D ShapesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336876731:3(1698-1712)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3368767
Min JKim SCho M(2023)Convolutional Hough Matching Networks for Robust and Efficient Visual CorrespondenceIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3233884(1-16)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2022.3233884
Min JCho M(2021)Convolutional Hough Matching Networks2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00296(2939-2949)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.00296
Qi CChen XLitany OGuibas L(2020)ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.00446(4403-4412)Online publication date: Jun-2020
https://doi.org/10.1109/CVPR42600.2020.00446
Shu ZXin SXu XLiu LKavan L(2019)Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoderIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.284862825:8(2583-2596)Online publication date: 1-Aug-2019
https://doi.org/10.1109/TVCG.2018.2848628
Qi CLitany OHe KGuibas L(2019)Deep Hough Voting for 3D Object Detection in Point Clouds2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00937(9276-9285)Online publication date: Oct-2019
https://doi.org/10.1109/ICCV.2019.00937
Carvalho Lvon Wangenheim A(2019)3D object recognition and classification: a systematic literature reviewPattern Analysis and Applications10.1007/s10044-019-00804-4Online publication date: 27-Feb-2019
https://doi.org/10.1007/s10044-019-00804-4
Zhi SLiu YLi XGuo YLavoué G(2017)LightNetProceedings of the Workshop on 3D Object Retrieval10.2312/3dor.20171046(9-16)Online publication date: 23-Apr-2017
https://dl.acm.org/doi/10.2312/3dor.20171046
Shu ZQi CXin SHu CWang LZhang YLiu L(2016)Unsupervised 3D shape segmentation and co-segmentation via deep learningComputer Aided Geometric Design10.1016/j.cagd.2016.02.01543:C(39-52)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.cagd.2016.02.015
Toony ZLaurendeau DGagné C(2015)Describing 3D Geometric Primitives Using the Gaussian Sphere and the Gaussian Accumulator3D Research10.1007/s13319-015-0074-36:4(1-22)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s13319-015-0074-3
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