Submission: GDRNPP-PBRReal-RGB-MModel/YCB-V/GDRNPP-PBRReal-RGB-MModel-YCBV

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Submission name GDRNPP-PBRReal-RGB-MModel-YCBV
Submission time (UTC) Oct. 8, 2022, 5 a.m.
User zyMeteroid
Task 6D localization of seen objects
Dataset YCB-V
Training model type Default
Training image type Synthetic + real
Description
Evaluation scores
AR:0.825
AR_MSPD:0.869
AR_MSSD:0.846
AR_VSD:0.760
average_time_per_image:0.118

Method: GDRNPP-PBRReal-RGB-MModel

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

GDRNPP for BOP2022

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).

In the PBRReal-RGB-MModel setting, for LMO, HB, ICBIN and ITODD datasets, we only use the provided synthetic training data (PBR) in training. While for YCBV, TUDL, TLESS, we use the provided real data and synthetic data (PBR) in training. MModel means for each dataset, we trained a separate model for each object.

For detection, we adopted yolox as the detection method. Otherwise, stronger data augmentation and ranger optimizer has been used.

For pose estimation, the difference between our GDRNPP and the CVPR-version GDR-Net mainly includes:

  • Domain Randomization: We used stronger domain randomization operations than the conference version during training.
  • Network Architecture: We used a more powerful backbone Convnext rather than resnet-34, and two mask heads for predicting amodal mask and visible mask separately.
  • Other training details, include learning rate, weight decay, visible threshold, and bounding box type.
Computer specifications GPU RTX 3090; CPU AMD EPYC 7H12 64-Core Processor.