Semantic Visual Localization

Understand

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.