Submission: ZebraPose-SAT/YCB-V

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Submission name
Submission time (UTC) Oct. 14, 2022, 12:38 p.m.
User zebrapose
Task Model-based 6D localization of seen objects
Dataset YCB-V
Training model type Default
Training image type Synthetic + real
Description
Evaluation scores
AR:0.828
AR_MSPD:0.875
AR_MSSD:0.845
AR_VSD:0.765
average_time_per_image:0.250

Method: ZebraPose-SAT

User zebrapose
Publication ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation, CVPR2022
Implementation https://github.com/suyz526/ZebraPose
Training image modalities RGB
Test image modalities RGB
Description

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