Submission: MegaPose-CNOS_fastSAM/YCB-V

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Submission name
Submission time (UTC) Sept. 4, 2023, 7:53 a.m.
User agimus-happypose
Task Model-based 6D localization of unseen objects
Dataset YCB-V
Training model type Default
Training image type Synthetic (provided)
Description Submission prepared by Médéric Fourmy, Elliot Maître, Yann Labbé
Evaluation scores
AR:0.601
AR_MSPD:0.705
AR_MSSD:0.578
AR_VSD:0.521
average_time_per_image:25.954

Method: MegaPose-CNOS_fastSAM

User agimus-happypose
Publication Labbé et al.: MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare, CoRL 2022
Implementation https://github.com/agimus-project/happypose
Training image modalities RGB
Test image modalities RGB
Description

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]:
- 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] Nguyen et al.: CNOS: A Strong Baseline for CAD-based Novel Object Segmentation, arXiv 2023
[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

Computer specifications NVIDIA Tesla V100 32Go