|Submission time (UTC)||Sept. 4, 2023, 7:54 a.m.|
|Task||6D localization of unseen objects|
|Training model type||Default|
|Training image type||Synthetic (custom)|
|Description||Submission prepared by Médéric Fourmy, Elliot Maître, Yann Labbé|
|Publication||Labbé et al.: MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare, CoRL 2022|
|Training image modalities||RGB|
|Test image modalities||RGB|
This submission was prepared by Mederic Fourmy, Elliot Maître, Lucas Manuelli, Yann Labbé
In this submission, CNOS_fastSAM [A] detections (default detections for Task 4) are used as input to the MegaPose pose estimation method [B].
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]:
Instructions to reproduce these results will be made available on https://github.com/agimus-project/happypose.
[A] Nguyen et al.: CNOS: A Strong Baseline for CAD-based Novel Object Segmentation, arXiv 2023
This work was granted access to the HPC resources of IDRIS under the allocation 011014301 made by GENCI
|Computer specifications||NVIDIA Tesla V100 32Go|