This submission was prepared by Mederic Fourmy, Elliot Maître, Lucas Manuelli, Yann Labbé

In this submission, GDRNPPDet_PBRReal, [A] detections (default detections for Task 1) are used as input to the MegaPose pose estimation method [B], without fine tuning on the core BOP challenge datasets.

For each detection, we run the MegaPose coarse network and refine the 1-best coarse estimate with 5 refiner iterations.

The following improvement has been made over the original MegaPose paper [B]:
- The orientation of the coarse hypotheses are obtained my discretizing SO(3) in 576 values using [C].

Instructions to reproduce these results will be made available on https://github.com/agimus-project/happypose.

[A] Liu et al.: https://github.com/shanice-l/gdrnpp_bop2022
[B] Labbé et al.: MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare, CoRL 2022
[C] Yershova et al.: Generating Uniform Incremental Grids on SO(3) Using the Hopf Fibration, IJRR 2009, reference implementation: http://lavalle.pl/software/so3/so3.html

This work was granted access to the HPC resources of IDRIS under the allocation 011014301 made by GENCI