Submission: GDRNPP-PBRReal-RGB-SModel/LM-O

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Submission name
Submission time (UTC) Oct. 16, 2022, 4:33 p.m.
User zyMeteroid
Task Model-based 6D localization of seen objects
Dataset LM-O
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Evaluation scores

Method: GDRNPP-PBRReal-RGB-SModel

User zyMeteroid
Publication Not yet
Implementation Pytorch, code can be found at
Training image modalities RGB
Test image modalities RGB

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-SModel 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. SModel means we trained a single model for each dataset.

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.