| Submission name | |||||||||||
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| Submission time (UTC) | Aug. 19, 2020, 2:03 a.m. | ||||||||||
| User | yann_labbe | ||||||||||
| 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 | yann_labbe |
|---|---|
| Publication | Labbé et al, CosyPose: Consistent multi-view multi-object 6D pose estimation, ECCV 2020 |
| Implementation | https://github.com/ylabbe/cosypose |
| Training image modalities | RGB |
| Test image modalities | RGB-D |
| Description | We apply ICP to each individual 6D pose estimates. The pose estimates are the ones from CosyPose-ECCV20-SYNT+REAL-1VIEW. We use parts of Pix2pose's ICP implementation https://github.com/kirumang/Pix2Pose/blob/master/tools/5_evaluation_bop_icp3d.py. MaskRCNN predicted masks are used to pick the 3D points that correspond to the detection in the depth. |
| Computer specifications | CPU: 20-core Intel Xeon 6164 @ 3.2 GHz, GPU: Nvidia V100 |