Submission: GDRNPP-PBR-RGBD-MModel/TUD-L

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Submission name
Submission time (UTC) Oct. 15, 2022, 11:51 a.m.
User zyMeteroid
Task 6D localization of seen objects
Dataset TUD-L
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Description
Evaluation scores
AR:0.929
AR_MSPD:0.970
AR_MSSD:0.961
AR_VSD:0.856
average_time_per_image:6.060

Method: GDRNPP-PBR-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 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.