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short-paper

Pose optimization in edge distance field for textureless 3D object tracking

Authors:

Xueying QinAuthors Info & Claims

CGI '17: Proceedings of the Computer Graphics International Conference

Article No.: 32, Pages 1 - 6

https://doi.org/10.1145/3095140.3095172

Published: 27 June 2017 Publication History

Abstract

This paper presents a monocular model-based 3D tracking approach for textureless objects. Instead of explicitly searching for 3D-2D correspondences as previous methods, which unavoidably generates individual outlier matches, we aim to minimize the holistic distance between the predicted object contour and the query image edges. We propose a method that can directly solve 3D pose parameters in unsegmented edge distance field. We derive the differentials of edge matching distance with respect to the pose parameters, and search the optimal 3D pose parameters using standard gradient-based non-linear optimization techniques. To avoid being trapped in local minima and to deal with potential large inter-frame motions, a particle filtering process with a first order autoregressive state dynamics is exploited. Occlusions are handled by a robust estimator. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and cluttered backgrounds.

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Cited By

Song XXie WLi JWang NZhong FZhang GQin X(2023)3D Object Tracking for Rough ModelsComputer Graphics Forum10.1111/cgf.1497642:7Online publication date: 30-Oct-2023
https://doi.org/10.1111/cgf.14976
Song XXie WLi JWang NZhong FZhang GQin X(2023)Minilag Filter for Jitter Elimination of Pose Trajectory in AR Environment2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR59233.2023.00111(950-959)Online publication date: 16-Oct-2023
https://doi.org/10.1109/ISMAR59233.2023.00111
Chen MWang PShang DWang P(2023)Cycle-attention-derain: unsupervised rain removal with CycleGANThe Visual Computer10.1007/s00371-023-02947-2Online publication date: 23-Jun-2023
https://doi.org/10.1007/s00371-023-02947-2
Show More Cited By

Index Terms

Pose optimization in edge distance field for textureless 3D object tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
  2. Computer graphics
    1. Graphics systems and interfaces
      1. Mixed / augmented reality

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Information & Contributors

Information

Published In

cover image ACM Other conferences

CGI '17: Proceedings of the Computer Graphics International Conference

June 2017

260 pages

ISBN:9781450352284

DOI:10.1145/3095140

Program Chairs:
Xiaoyang Mao
University of Yamanashi, Japan
,
Daniel Thalmann
NTU, Singapore & EPFL IC-DO, Switzerland
,
Marina Gavrilova
University of Calgary, Canada

Copyright © 2017 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2017

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Short-paper

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CGI '17

CGI '17: Computer Graphics International 2017

June 27 - 30, 2017

Yokohama, Japan

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Overall Acceptance Rate 35 of 159 submissions, 22%

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Citations

Cited By

Song XXie WLi JWang NZhong FZhang GQin X(2023)3D Object Tracking for Rough ModelsComputer Graphics Forum10.1111/cgf.1497642:7Online publication date: 30-Oct-2023
https://doi.org/10.1111/cgf.14976
Song XXie WLi JWang NZhong FZhang GQin X(2023)Minilag Filter for Jitter Elimination of Pose Trajectory in AR Environment2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR59233.2023.00111(950-959)Online publication date: 16-Oct-2023
https://doi.org/10.1109/ISMAR59233.2023.00111
Chen MWang PShang DWang P(2023)Cycle-attention-derain: unsupervised rain removal with CycleGANThe Visual Computer10.1007/s00371-023-02947-2Online publication date: 23-Jun-2023
https://doi.org/10.1007/s00371-023-02947-2
Huang HZhong FQin X(2022)Pixel-Wise Weighted Region-Based 3D Object Tracking Using Contour ConstraintsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.308519728:12(4319-4331)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3085197
Li JWang BZhu SCao XZhong FChen WLi TGu JQin X(2022)BCOT: A Markerless High-Precision 3D Object Tracking Benchmark2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00658(6687-6696)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00658
Liu CGuo WHu WChen RLiu J(2021)Real-Time Model-Based Monocular Pose Tracking for an Asteroid by Contour FittingIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2020.304411657:3(1538-1561)Online publication date: Jun-2021
https://doi.org/10.1109/TAES.2020.3044116
Li JZhong FXu SQin X(2021)3D Object Tracking with Adaptively Weighted Local BundlesJournal of Computer Science and Technology10.1007/s11390-021-1272-536:3(555-571)Online publication date: 31-May-2021
https://doi.org/10.1007/s11390-021-1272-5
Huang HZhong FSun YQin X(2020)An Occlusion‐aware Edge‐Based Method for Monocular 3D Object Tracking using Edge ConfidenceComputer Graphics Forum10.1111/cgf.1415439:7(399-409)Online publication date: 24-Nov-2020
https://doi.org/10.1111/cgf.14154
Zhong LZhao XZhang YZhang SZhang L(2020)Occlusion-Aware Region-Based 3D Pose Tracking of Objects With Temporally Consistent Polar-Based Local PartitioningIEEE Transactions on Image Processing10.1109/TIP.2020.297351229(5065-5078)Online publication date: 2020
https://doi.org/10.1109/TIP.2020.2973512
Zhong LZhang YZhao HChang AXiang WZhang SZhang L(2020)Seeing Through the Occluders: Robust Monocular 6-DOF Object Pose Tracking via Model-Guided Video Object SegmentationIEEE Robotics and Automation Letters10.1109/LRA.2020.30038665:4(5159-5166)Online publication date: Oct-2020
https://doi.org/10.1109/LRA.2020.3003866
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