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

Submission name
Submission time (UTC) Oct. 14, 2022, 6:38 a.m.
User zyMeteroid
Task Pose estimation (BOP 2019-2022)
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-Fast

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

GDRNPP for BOP2022 (Fast Version)

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 GDRNPP_PBR_RGB_MModel, we utilize depth information to further refine the estimated pose. In order to fulfil the real-time application requirements, we implement a fast refinement module. 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.