|Submission time (UTC)||Oct. 15, 2022, 11:50 a.m.|
|Task||6D localization of seen objects|
|Training model type||Default|
|Training image type||Synthetic (only PBR images provided for BOP Challenge 2020 were used)|
|Implementation||Pytorch, code can be found at https://github.com/shanice-l/gdrnpp_bop2022|
|Training image modalities||RGB-D|
|Test image modalities||RGB-D|
GDRNPP for BOP2022 (Trained with Only PBR Data)
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_PBR_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.|