Submitted to: BOP Challenge 2023

Training data: real + provided PBR

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

Notes:

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.