| Submission name | |||||||||||||||||||||||||||
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| Submission time (UTC) | May 4, 2022, 4:25 p.m. | ||||||||||||||||||||||||||
| User | yann_labbe | ||||||||||||||||||||||||||
| Task | 2D segmentation of seen objects | ||||||||||||||||||||||||||
| Dataset | T-LESS | ||||||||||||||||||||||||||
| Training model type | CAD | ||||||||||||||||||||||||||
| Training image type | Synthetic + real | ||||||||||||||||||||||||||
| Description | |||||||||||||||||||||||||||
| Evaluation scores |
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| User | yann_labbe |
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| 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 |
| Description | The method is the same as CosyPose-ECCV20-PBR-1VIEW but we also add the additionnal real and synthetic images to the training data when an official training split is available: TUD-L, T-LESS and YCB-Video. On other datasets, the results reported are the same as CosyPose-ECCV20-1VIEW-PBR. The models (detectors, coarse pose estimation, refiner) are pre-trained from the models trained on PBR images only. |
| Computer specifications | CPU: 20-core Intel Xeon 6164 @ 3.2 GHz, GPU: Nvidia V100 |