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Submission time (UTC) | May 24, 2022, 5:27 p.m. | ||||||||||||||||||||||||||
User | yann_labbe | ||||||||||||||||||||||||||
Task | Model-based 2D segmentation of seen objects | ||||||||||||||||||||||||||
Dataset | HB | ||||||||||||||||||||||||||
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 |
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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 |