| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Oct. 15, 2022, 11:51 a.m. | ||||||||||
| User | zyMeteroid | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | TUD-L | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | |||||||||||
| Evaluation scores |
|
| User | zyMeteroid |
|---|---|
| Publication | Not yet |
| Implementation | Pytorch, code can be found at https://github.com/shanice-l/gdrnpp_bop2022 |
| Training image modalities | RGB-D |
| Test image modalities | RGB-D |
| Description | GDRNPP for BOP2022 (Trained with Only PBR Data) Authors: Xingyu Liu, Ruida Zhang, Chenyangguang Zhang, Bowen Fu, Jiwen Tang, Xiquan Liang, Jingyi Tang, Xiaotian Cheng, Yukang Zhang, Gu Wang, and Xiangyang Ji (Tsinghua University). Based on GDPNPP_PBR_RGB_MModel, we utilize depth information to further refine the estimated pose. We adopt depth refinement inspired by Coupled Iterative Refinement. |
| Computer specifications | GPU RTX 3090; CPU AMD EPYC 7H12 64-Core Processor. |