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

Public submissions

Date Submission name Dataset
2022-10-14 12:35 - LM-O
2022-10-14 12:38 - YCB-V
2022-10-14 12:40 - T-LESS
2022-10-14 12:41 - TUD-L
2022-10-14 12:44 - IC-BIN
2022-10-14 15:19 - ITODD
2022-10-15 20:25 - HB