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. Then, each of the top-5 hypotheses are refined using 5 iterations of the refinement network. The refined hypotheses are then scored using the coarse network and the best one is considered the pose estimate for the initial detection.

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].
- The refinement network is ran on multiple pose hypotheses coming from the coarse network.

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