| 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 BOP 2022 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_PBRReal_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. |
| Date | Submission name | Dataset | ||
|---|---|---|---|---|
| 2022-10-12 04:11 | - | TUD-L | ||
| 2022-10-13 02:23 | - | T-LESS | ||
| 2022-10-14 06:56 | - | YCB-V | ||
| 2022-10-14 06:57 | - | LM-O | ||
| 2022-10-14 06:58 | - | IC-BIN | ||
| 2022-10-14 06:59 | - | HB | ||
| 2022-10-15 11:52 | - | ITODD |