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Submission time (UTC) | Oct. 15, 2022, 6:09 p.m. | ||||||||||
User | zebrapose | ||||||||||
Task | Model-based 6D localization of seen objects | ||||||||||
Dataset | LM-O | ||||||||||
Training model type | Default | ||||||||||
Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
Description | |||||||||||
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 |