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Submission time (UTC) | Oct. 16, 2022, 6:37 p.m. | ||||||||||||||||||||||||||
User | zebrapose | ||||||||||||||||||||||||||
Task | Model-based 2D segmentation of seen objects | ||||||||||||||||||||||||||
Dataset | LM-O | ||||||||||||||||||||||||||
Training model type | Default | ||||||||||||||||||||||||||
Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||||||||||||||||||
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Evaluation scores |
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User | zebrapose |
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Publication | ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation, CVPR2022 |
Implementation | https://github.com/suyz526/ZebraPose |
Training image modalities | RGB |
Test image modalities | RGB |
Description | Based on the paper "ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation", CVPR 2022.
List of contributors:
Yongzhi Su, Praveen Nathan, Torben Fetzer, Jason Rambach, Didier Stricker
Mahdi Saleh, Yan Di, Nassir Navab, Benjamin Busam, Federico Tombari
Yongliang Lin, Yu Zhang |
Computer specifications | Intel(R) Xeon(R) E-2146G CPU @ 3.50GHz, Nvidia RTX2080Ti |