| Submission name | |||||||||||
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| Submission time (UTC) | Aug. 19, 2020, 5:33 p.m. | ||||||||||
| User | MartinSmeyer | ||||||||||
| Task | 6D localization of seen objects | ||||||||||
| Dataset | ITODD | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (custom) | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | MartinSmeyer |
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| Publication | https://openaccess.thecvf.com/content_CVPR_2020/html/Sundermeyer_Multi-Path_Learning_for_Object_Pose_Estimation_Across_Domains_CVPR_2020_paper.html |
| Implementation | https://github.com/DLR-RM/AugmentedAutoencoder/tree/multipath |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | A single MultiPath Encoder model to predict the poses of all objects of all datasets in the BOP challenge (instead of training one model per object/dataset). Single object OpenGL renderings are used for training. The single MultiPath Encoder achieves results similar to 108 separately trained AAE models used in the BOP19 challenge on pure RGB data. This result should serve as a baseline for learning-based pose-sensitive feature extraction that can scale to a large number of trained & untrained objects. For instance segmentation we use dataset-wise trained MaskRCNNs (trained on BlenderProc PBR and real data). |
| Computer specifications | Intel(R) Xeon(R) W-2133 CPU @ 3.60GHz + Intel Nvidia 1080 Ti |