(Privacy-Preserving) Visual Localization
Visual localization is the problem of estimating the precise position and orientation from which a given image was taken. Solving the visual localization step is a fundamental component of technologies such as Augmented and Virtual Reality systems and autonomous robots such as self-driving cars. After an introduction to the visual localization problem, including an overview over different solution strategies for the localization problem, this talk focuses on the sub-field of privacy-preserving localization. Given the rising need for and availability of cloud-based localization services (e.g., offered by Google, Microsoft, and Niantic), privacy-preserving localization approaches aim to ensure that no private details can be recovered from user data shared with such services. This data is typically in the form of images or user-generated 3D maps. We will give a brief overview over existing privacy-preserving approaches and will then take a critical look at whether these approaches are as privacy-preserving as advertised.
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Torsten Sattler is a Senior Researcher at Czech Technical University in Prague. Before, he was a tenured associate professor at Chalmers University of Technology. He received a PhD in Computer Science from RWTH Aachen University, Germany, in 2014. From December 2013 to December 2018, he was a post-doctoral and senior researcher at ETH Zurich. Torsten has extensively worked on feature-based localization methods, long-term localization (see also the benchmarks at visuallocalization.net), localization on mobile devices, learning local features, and using semantic scene understanding for localization. Recently, his group started focusing on neural radiance fields and other neural scene representations. Torsten has co-organized tutorials and workshops at CVPR, ECCV, and ICCV, and was / is an area chair for CVPR, ICCV, 3DV, GCPR, ICRA, and ECCV. He was a program chair for DAGM GCPR’20, a general chair for 3DV’22, and will be a program chair for ECCV’24.