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