Submission name | |||||||||||
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Submission time (UTC) | Nov. 12, 2023, 9:54 a.m. | ||||||||||
User | Tuebel | ||||||||||
Task | Model-based 6D localization of unseen objects | ||||||||||
Dataset | HB | ||||||||||
Training model type | Default | ||||||||||
Training image type | None | ||||||||||
Description | |||||||||||
Evaluation scores |
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User | Tuebel |
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Publication | |
Implementation | |
Training image modalities | None |
Test image modalities | D |
Description | Submitted to: BOP Challenge 2023 Training data: None Onboarding data: None Used 3D models: CAD or default Notes: The localization-only method requires a separate detector and is only tested on Task 4 with default segmentation masks from CNOS. This version runs the pose inference at most for inst_count instances from test_targets_bop19.json. It uses a sequential Monte Carlo (SMC) sampler and the CAD model to estimate the pose using only the segmentation mask and the depth image. The GPU is utilized to render depth images for pose hypotheses and evaluate their likelihood in parallel. The sampler had a time budget of 0.5s per object pose inference. Part of my dissertation: T. Redick, „Bayesian inference for CAD-based pose estimation on depth images for robotic manipulation“, RWTH Aachen University, 2024. doi: 10.18154/RWTH-2024-04533. Code: https://github.com/rwth-irt/BayesianPoseEstimation.jl Contact: tim.redick@rwth-aachen.de |
Computer specifications | AMD Ryzen Threadripper PRO 5975WX, NVIDIA RTX 4090 |