Submission: GDRNPP-PBRReal-RGBD-MModel-OfficialDet/LM-O

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Submission name
Submission time (UTC) Oct. 12, 2022, 4:08 a.m.
User zyMeteroid
Task 6D localization of seen objects
Dataset LM-O
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Evaluation scores

Method: GDRNPP-PBRReal-RGBD-MModel-OfficialDet

User zyMeteroid
Publication Not yet
Implementation Pytorch, code can be found at
Training image modalities RGB-D
Test image modalities RGB-D

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.