| Submission name | PPF_3D_ICP | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | March 3, 2022, 1:37 p.m. | ||||||||||
| User | yuecideng | ||||||||||
| Task | Pose estimation (BOP 2019-2022) | ||||||||||
| Dataset | LM-O | ||||||||||
| Training model type | CAD | ||||||||||
| Training image type | None | ||||||||||
| Description | use segmentation result to obtain mask of object instance, then use PPF+ICP for pose estimation, | ||||||||||
| Evaluation scores |
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| User | yuecideng |
|---|---|
| Publication | |
| Implementation | https://github.com/yuecideng/Misc3D |
| Training image modalities | D |
| Test image modalities | D |
| Description | A modified implementation of paper "Drost et al., Model globally, match locally: Efficient and robust 3D object recognition, CVPR 2010". Add sparse icp and dense icp for post processing. |
| Computer specifications | intel@core i7-1165G7 2.8Gz 8 threads |