Submitted to: BOP Challenge 2023

Training data: real + provided PBR

Used 3D models: Reconstructed for T-LESS, default for other datasets

Notes:

Setting - One network per object was trained

2D Bounding Box - Default detections of 2023

Modifications to the original ZebraPose paper -

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.

We replace the Resnet34 backbone with EffnetB4 in ZebraPose. (Only replace the backbone in the pose estimation part)

For the refinement - We use the refinement from https://github.com/shanice-l/gdrnpp_bop2022/tree/pose_refine .

List of contributors -

Praveen Annamalai Nathan, Sandeep Prudhvi Krishna Inuganti,Yongliang Lin, Yongzhi Su,Yu Zhang, Didier Stricker, Jason Rambach