PointPoseNet:Point pose network for robust 6D object pose estimation

Wei Chen; Jinming Duan; Hector Basevi; Hyung Jin Chang; Ales Leonardis

doi:10.1109/WACV45572.2020.9093272

PointPoseNet:Point pose network for robust 6D object pose estimation

Wei Chen^*, Jinming Duan, Hector Basevi, Hyung Jin Chang, Ales Leonardis

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.

Original language	English
Title of host publication	Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2813-2822
Number of pages	10
ISBN (Electronic)	9781728165530
DOIs	https://doi.org/10.1109/WACV45572.2020.9093272
Publication status	Published - Mar 2020
Event	2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States Duration: 1 Mar 2020 → 5 Mar 2020

Publication series

Name	Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

Conference

Conference	2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
Country/Territory	United States
City	Snowmass Village
Period	1/03/20 → 5/03/20

Bibliographical note

Funding Information:
Acknowledgement We acknowledge MoD/Dstl and EP-SRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project.

Funding Information:
We acknowledge MoD/Dstl and EPSRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/WACV45572.2020.9093272

Cite this

Chen, W., Duan, J., Basevi, H., Chang, H. J., & Leonardis, A. (2020). PointPoseNet:Point pose network for robust 6D object pose estimation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 2813-2822). Article 9093272 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WACV45572.2020.9093272

Chen, Wei ; Duan, Jinming ; Basevi, Hector et al. / PointPoseNet:Point pose network for robust 6D object pose estimation. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. pp. 2813-2822 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).

@inproceedings{905c0bc61be44985908e51aab414dcd8,

title = "PointPoseNet:Point pose network for robust 6D object pose estimation",

abstract = "In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.",

author = "Wei Chen and Jinming Duan and Hector Basevi and Chang, {Hyung Jin} and Ales Leonardis",

note = "Funding Information: Acknowledgement We acknowledge MoD/Dstl and EP-SRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project. Funding Information: We acknowledge MoD/Dstl and EPSRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 ; Conference date: 01-03-2020 Through 05-03-2020",

year = "2020",

month = mar,

doi = "10.1109/WACV45572.2020.9093272",

language = "English",

series = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "2813--2822",

booktitle = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",

}

Chen, W, Duan, J , Basevi, H , Chang, HJ & Leonardis, A 2020, PointPoseNet:Point pose network for robust 6D object pose estimation. in Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020., 9093272, Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Institute of Electrical and Electronics Engineers (IEEE), pp. 2813-2822, 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass Village, United States, 1/03/20. https://doi.org/10.1109/WACV45572.2020.9093272

PointPoseNet:Point pose network for robust 6D object pose estimation. / Chen, Wei; Duan, Jinming ; Basevi, Hector et al.
Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 2813-2822 9093272 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - PointPoseNet:Point pose network for robust 6D object pose estimation

AU - Chen, Wei

AU - Duan, Jinming

AU - Basevi, Hector

AU - Chang, Hyung Jin

AU - Leonardis, Ales

N1 - Funding Information: Acknowledgement We acknowledge MoD/Dstl and EP-SRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project. Funding Information: We acknowledge MoD/Dstl and EPSRC (EP/N019415/1) for providing the grant to support the UK academics involvement in MURI project. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/3

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N2 - In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.

AB - In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.

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M3 - Conference contribution

AN - SCOPUS:85085495782

T3 - Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

SP - 2813

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BT - Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020

Y2 - 1 March 2020 through 5 March 2020

ER -

Chen W, Duan J , Basevi H , Chang HJ , Leonardis A. PointPoseNet:Point pose network for robust 6D object pose estimation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers (IEEE). 2020. p. 2813-2822. 9093272. (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020). doi: 10.1109/WACV45572.2020.9093272

PointPoseNet:Point pose network for robust 6D object pose estimation

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this