| Submission name | with instance segmentation | ||||||||||
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| Submission time (UTC) | Oct. 12, 2019, 1:44 p.m. | ||||||||||
| User | ivan_shugurov | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | T-LESS | ||||||||||
| Training model type | CAD | ||||||||||
| Training image type | Synthetic (custom) | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | ivan_shugurov |
|---|---|
| Publication | Sergey Zakharov, et al. DPOD: Dense 6D Pose Object Detector in RGB images, ICCV, 2019 |
| Implementation | http://campar.in.tum.de/Main/IvanShugurov |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | One network per scene was trained. No refininement was used. For the scenes which include multiple instances of the same object, object boundaries were additionally predicted. For the linemod objects the UV maps described in the paper were used, for the other datasets normalized 3D coordinates were used instead. The networks were trained on synthetic data. For training on the TLESS dataset, only the provided textureless CAD models were used. |
| Computer specifications | Max. 16 Threads on Intel Core i9-9900K, RTX 2080 Ti |