| Submission name | |||||||||||
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| Submission time (UTC) | Oct. 13, 2022, 2:22 a.m. | ||||||||||
| User | zyMeteroid | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | ITODD | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | zyMeteroid |
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| 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 with Official Detection Authors: Xingyu Liu, Ruida Zhang, Chenyangguang Zhang, Bowen Fu, Jiwen Tang, Xiquan Liang, Jingyi Tang, Xiaotian Cheng, Yukang Zhang, Gu Wang, and Xiangyang Ji. We test GDPNPP_PBRReal_RGB_MModel with official detection provided by bop2022. Then 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. |