| Submission name | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Oct. 17, 2019, 7:05 a.m. | ||||||||||
| User | Berti | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | YCB-V | ||||||||||
| 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 | D |
| Description | An implementation of the paper, including ICP for post-processing. The parameters are set to a "fast" mode (relative sampling distance of 0.05 for model creation and matching). No images are used, the method uses depth only. The training is done only on the CAD models. The method uses only the CPU, no GPU. Additionally, the depth range for each dataset is used to limit the search range by thresholding the z-images prior to the search. Implementation: HALCON 19.05 progress. |
| Computer specifications | Max. 12 Threads on intel Xeon CPU E5-2690 v4 @ 2.60GHz, 2601 Mhz |