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[bibtex]@InProceedings{Stoiber_2020_ACCV, author = {Stoiber, Manuel and Pfanne, Martin and Strobl, Klaus H. and Triebel, Rudolph and Albu-Schaeffer, Alin}, title = {A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }
A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking
Abstract
We propose a novel, highly efficient sparse approach to region-based 6DoF object tracking that requires only a monocular RGB camera and the 3D object model. The key contribution of our work is a probabilistic model that considers image information sparsely along correspondence lines. For the implementation, we provide a highly efficient discrete scale-space formulation. In addition, we derive a novel mathematical proof that shows that our proposed likelihood function follows a Gaussian distribution. Based on this information, we develop robust approximations for the derivatives of the log-likelihood that are used in a regularized Newton optimization. In multiple experiments, we show that our approach outperforms state-of-the-art region-based methods in terms of tracking success while being about one order of magnitude faster. The source code of our tracker is publicly available.
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