| Submission name | FFB6D-lmo-synthetic | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Dec. 3, 2021, 3:47 p.m. | ||||||||||
| User | fegorsch | ||||||||||
| Task | Pose estimation (BOP 2019-2022) | ||||||||||
| Dataset | LM-O | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (provided) | ||||||||||
| Description | Trained for 316600 iterations at 0.5 seconds per iteration, about 7 days. | ||||||||||
| Evaluation scores |
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| User | fegorsch |
|---|---|
| Publication | Yisheng He et al.: FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021 |
| Implementation | https://github.com/ethnhe/FFB6D |
| Training image modalities | RGB-D |
| Test image modalities | RGB-D |
| Description | FFB6D trained with purely synthetic BlenderProc-images. |
| Computer specifications | AMD Ryzen 5 3600X 6-Core Processor, NVIDIA TITAN RTX |