Submission: GDRNPP-PBR-RGB-MModel/YCB-V/ycbv_pbr_rgb_mmodel

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Submission name ycbv_pbr_rgb_mmodel
Submission time (UTC) Oct. 13, 2022, 6:31 a.m.
User zyMeteroid
Task 6D localization of seen objects
Dataset YCB-V
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Description multi model for one dataset
Evaluation scores
AR:0.713
AR_MSPD:0.831
AR_MSSD:0.708
AR_VSD:0.599
average_time_per_image:0.277

Method: GDRNPP-PBR-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 PBR_RGB_MModel setting, all models are trained only using the provided PBR synthetic data. 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.