| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Aug. 12, 2019, 12:24 p.m. | ||||||||||
| User | Berti | ||||||||||
| 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 | Berti |
|---|---|
| Publication | Drost et al., Model globally, match locally: Efficient and robust 3D object recognition, CVPR 2010. |
| Implementation | |
| Training image modalities | None |
| Test image modalities | RGB-D |
| Description | An implementation of the paper, including - ICP for post-processing - using 3D surface and 3D edges for voting - using 3D surface, 3D edges and 2D images for refinement The parameters are set to a slower, but more accurate mode (relative sampling distance of 0.03 for model creation and matching). CPU only, no GPU is used. Implementation: HALCON 19.05 progress. |
| Computer specifications | Max. 12 Threads on intel Xeon CPU E5-2690 v4 @ 2.60GHz, 2601 Mhz |