Submission: Koenig-Hybrid-DL-PointPairs/T-LESS

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Submission name
Submission time (UTC) Aug. 19, 2020, 8:23 a.m.
User rko
Task Model-based 6D localization of seen objects
Dataset T-LESS
Training model type Reconstucted
Training image type Synthetic (custom)
Evaluation scores

Method: Koenig-Hybrid-DL-PointPairs

User rko
Publication König, Drost: A Hybrid Approach for 6DoF Pose Estimation, ECCV 2020 Workshops
Training image modalities RGB
Test image modalities RGB-D

A hybrid model that first finds object instances using either RetinaMask [1] or MaskRCNN [2], whichever has the better mAP on the validation set. For each instance the point-pair voting of [3] is used to recover the pose. For datasets with textured models, symmetries are resolved using the descriptors of [4]. For [3], we use the implementation in MVTec HALCON 20.05.

[1] Fu, C. Y., Shvets, M., & Berg, A. C. (2019). RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free. arXiv preprint arXiv:1901.03353. [2] He, K., Gkioxari, G., Dollár, P., & Girshick, R.: Mask r-cnn. ICCV 2017. [3] Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: Efficient and robust 3d object recognition. CVPR 2010. [4] Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. T-PAMI 2006.

Computer specifications Nvidia GeForce GTX 1080 Ti; Max. 12 Threads on Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz