| Submission name | |||||||||||
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| Submission time (UTC) | Sept. 24, 2023, 8:55 a.m. | ||||||||||
| User | zyMeteroid | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | TUD-L | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | zyMeteroid |
|---|---|
| Publication | not yet |
| Implementation | Pytorch |
| Training image modalities | RGB-D |
| Test image modalities | RGB-D |
| Description | Submitted to: BOP Challenge 2023 Training data: real + provided PBR Used 3D models: Reconstructed for T-LESS, default for other datasets Notes: Authors: Ruida Zhang, Ziqin Huang, Gu Wang, Xingyu Liu, Chenyangguang Zhang and Xiangyang Ji. (Tsinghua University) GDRNPP + coordinate-guided depth refinement, using default detection. A network is trained for each object. |
| Computer specifications | GPU RTX 3090; CPU AMD EPYC 7H12 64-Core Processor. |