Submission: FFB6D-PBR+Real/LM-O/submission1
| Submission name |
submission1
|
| Submission time (UTC) |
March 9, 2023, 5:52 a.m.
|
| User |
CW_FLOYD
|
| Task |
Pose estimation (BOP 2019-2022) |
| Dataset |
LM-O |
| Training model type |
Default |
| Training image type |
Synthetic (only PBR images provided for BOP Challenge 2020 were used) |
| Description |
We trained FFB6D model with PBR and real dataset.
|
| Evaluation scores |
| AR: | 0.677 |
| AR_MSPD: | 0.776 |
| AR_MSSD: | 0.723 |
| AR_VSD: | 0.533 |
| average_time_per_image: | 0.057 |
|
| User |
CW_FLOYD
|
| Publication |
ffb6d: a full flow bidirectional fusion network for 6d pose estimation, he et al., CVPR, 2021 |
| Implementation |
|
| Training image modalities |
RGB-D |
| Test image modalities |
RGB-D |
| Description |
|
| Computer specifications |
NVIDIA RTX A4000 |