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Date
2018Type
- Conference Paper
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. Show more
Publication status
publishedExternal links
Book title
2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionPages / Article No.
Publisher
IEEEEvent
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
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