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Submission time (UTC) | Sept. 26, 2023, 3:02 p.m. | ||||||||||
User | agimus-happypose | ||||||||||
Task | Model-based 6D localization of unseen objects | ||||||||||
Dataset | HB | ||||||||||
Training model type | Default | ||||||||||
Training image type | Synthetic (custom) | ||||||||||
Description | Submission prepared by Médéric Fourmy, Elliot Maître, Yann Labbé | ||||||||||
Evaluation scores |
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User | agimus-happypose |
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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 Elliot Maître, Mederic Fourmy, 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. 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] 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 |