Submission name | |||||||||||
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Submission time (UTC) | Sept. 22, 2023, 3:59 p.m. | ||||||||||
User | agimus-happypose | ||||||||||
Task | Model-based 6D localization of seen objects | ||||||||||
Dataset | ITODD | ||||||||||
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 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]: 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 This work was granted access to the HPC resources of IDRIS under the allocation 011014301 made by GENCI |
Computer specifications | NVIDIA Tesla V100 32Go |