| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Dec. 27, 2021, 8:01 a.m. | ||||||||||
| User | YishengHe | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | LM | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | FFB6D is a full flow bidirectional fusion network designed for 6D pose estimation from a single RGBD image. We only use the PBR data for training. The predicted result is not refined by any iterative refinement algorithms, i.e., ICP. Difference from the original FFB6D: - Use a single unified network for all object classes as all GT labels are provided in PBR. - Regenerate object SIFT-FPS 3D keypoint from BOP mesh models as their object coordinates are different from the original. | ||||||||||
| Evaluation scores |
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| User | YishengHe |
|---|---|
| Publication | Yisheng He et al.: FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation, CVPR 2021 (Oral) |
| Implementation | https://github.com/ethnhe/FFB6D |
| Training image modalities | RGB-D |
| Test image modalities | RGB-D |
| Description | FFB6D is a full flow bidirectional fusion network designed for 6D pose estimation from a single RGBD image. We only use the PBR data for training. The predicted result is not refined by any iterative refinement algorithms, i.e., ICP. |
| Computer specifications | CPU: Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz; GPU: GeForce RTX 2080Ti |