| Submission name | PPF_3D_ICP | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | March 6, 2022, 2:55 p.m. | ||||||||||
| User | yuecideng | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | TUD-L | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | None | ||||||||||
| Description | |||||||||||
| 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 |