Submission: GDRNPP-PBRReal-RGBD-SModel/LM-O

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Submission name
Submission time (UTC) Oct. 12, 2022, 9:49 a.m.
User zyMeteroid
Task Model-based 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-SModel

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

Submitted to: BOP Challenge 2023

Training data: real + provided PBR

Used 3D models: Reconstructed for T-LESS, default for other datasets


Authors: Xingyu Liu, Ruida Zhang, Chenyangguang Zhang, Bowen Fu, Jiwen Tang, Xiquan Liang, Jingyi Tang, Xiaotian Cheng, Yukang Zhang, Gu Wang, and Xiangyang Ji.

Based on GDRNPP-PBRReal-RGB-SModel, we utilize depth information to further refine the estimated pose. We implement a fast refinement module without learned parameters. We compare the rendered object depth and the observed depth to refine translation.

Computer specifications GPU RTX 3090; CPU AMD EPYC 7H12 64-Core Processor.