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arXiv · 1712.05773

Semantic Visual Localization

Johannes L. Schönberger·Marc Pollefeys·Andreas Geiger·Torsten Sattler
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Abstract

Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting 3D descriptors are robust to missing observations by encoding high-level 3D geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes.

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Johannes L. Schönberger, Marc Pollefeys, Andreas Geiger, Torsten Sattler. 2018-04-16. Semantic Visual Localization. https://arxiv.org/abs/1712.05773

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