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Submission time (UTC) | Oct. 17, 2023, 5:24 a.m. | ||||||||||
User | sp9103 | ||||||||||
Task | Model-based 6D localization of unseen objects | ||||||||||
Dataset | HB | ||||||||||
Description | |||||||||||
Evaluation scores |
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User | sp9103 |
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Publication | Genflow: Generalizable recurrent flow for 6d pose refinement of novel objects, CVPR 2024 |
Implementation | - |
Training image modalities | RGB-D |
Test image modalities | RGB |
Description | Submitted to: BOP Challenge 2023 Training data: MegaPose-GSO and MegaPose-ShapeNetCore Onboarding data: No Used 3D models: Default, CAD Notes: In this submission, CNOS_fastSAM [A] detections are used as the input to our pose estimation method. Our pose estimation method uses the coarse-to-fine strategy following the MegaPose [B] structure. A single model is used for all datasets. We use the multi-hypotheses method proposed in the MegaPose [B]. The details are as follows: For each detection, we extract top-16 hypotheses from our coarse network, and each hypothesis is refined using the refinement network. The refined hypotheses are scored using the coarse network, and the best one is considered the output. Our coarse network is based on the MegaPose [B] coarse network. The main differences from the original MegaPose paper are as follows:
Our refinement network is based on the Shape-Constraint Recurrent Flow framework [C]. It estimates the flow from the rendered image to the input. The main differences from the original SCFlow paper are as follows:
Note that the inputs to our neural networks are the rgb images only, and the depth images in the training dataset are used to train the visibility mask. In this submission, each coarse hypothesis is refined 5 times. [A] Nguyen et al.: CNOS: A Strong Baseline for CAD-based Novel Object Segmentation, arXiv 2023 List of contributors: Sungphill Moon (sungphill.moon@naverlabs.com), Hyeontae Son (son.ht@naverlabs.com) If you have any questions, feel free to contact us. |
Computer specifications | GPU V100; CPU Intel Xeon Gold6248@2.5G |