| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Aug. 19, 2020, 6:27 p.m. | ||||||||||
| User | thodan | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | IC-BIN | ||||||||||
| 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 | thodan |
|---|---|
| Publication | Hodan, Barath, Matas, EPOS: Estimating 6D Pose of Objects with Symmetries, CVPR 2020 |
| Implementation | https://github.com/thodan/epos |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | Only the synthetic "PBR-BlenderProc4BOP" RGB images provided for the BOP Challenge 2020 were used for training. The results were achieved without any post-refinement of the estimated poses (i.e. without ICP, DeepIM, etc.). Up to 5000 2D-3D correspondences with the highest confidence were used per image. Other hyper-parameters were set as described in the CVPR 2020 paper. |
| Computer specifications | 14-core Intel Xeon E5-2680 v4 CPU, 252GB RAM, Nvidia P100 GPU |