Submission: GDRNPP-PBRReal-RGBD-MModel/YCB-V

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Submission name
Submission time (UTC) Oct. 14, 2022, 6:56 a.m.
User zyMeteroid
Task Model-based 6D localization of seen objects
Dataset YCB-V
Training model type Default
Training image type Synthetic + real
Description
Evaluation scores
AR:0.921
AR_MSPD:0.919
AR_MSSD:0.957
AR_VSD:0.886
average_time_per_image:8.000

Method: GDRNPP-PBRReal-RGBD-MModel

User zyMeteroid
Publication Not yet
Implementation Pytorch, code can be found at https://github.com/shanice-l/gdrnpp_bop2022
Training image modalities RGB-D
Test image modalities RGB-D
Description

GDRNPP for BOP 2022

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_PBRReal_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.