Submission: ZebraPose-SAT/ITODD

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Submission name
Submission time (UTC) Oct. 14, 2022, 3:19 p.m.
User zebrapose
Task 6D localization of seen objects
Dataset ITODD
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Evaluation scores

Method: ZebraPose-SAT

User zebrapose
Publication ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation, CVPR2022
Training image modalities RGB
Test image modalities RGB

Based on the paper "ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation", CVPR 2022.

  • Training images: PBR image + Real images (if available)

  • Setting: One network per object was trained

  • 2D Bounding Box: FCOS detection results provided by CDPNv2

  • Modifications to the original ZebraPose paper:

    1. Added Symmetry-Aware Training (SAT). The network and loss functions are not changed. There will be a new ground truth for the sym. objects, details can be found in the Github Repository. Special thanks to Yongliang Lin for his contribution.
  • The reported inference time included 2D detection time.

List of contributors:

  • German Research Center for Artificial Intelligence (DFKI), Augmented Vision department:

Yongzhi Su, Praveen Nathan, Torben Fetzer, Jason Rambach, Didier Stricker

  • Technical University Munich (TUM), CAMPAR:

Mahdi Saleh, Yan Di, Nassir Navab, Benjamin Busam, Federico Tombari

  • Zhejiang University (ZJU):

Yongliang Lin, Yu Zhang

Computer specifications Intel(R) Xeon(R) E-2146G CPU @ 3.50GHz, Nvidia RTX2080Ti