Hybrid scene Compression for Visual Localization
Paper in proceedings, 2019

Localizing an image w.r.t. a 3D scene model represents a core task for many computer vision applications. An increasing number of real-world applications of visual localization on mobile devices, e.g., Augmented Reality or autonomous robots such as drones or self-driving cars, demand localization approaches to minimize storage and bandwidth requirements. Compressing the 3D models used for localization thus becomes a practical necessity. In this work, we introduce a new hybrid compression algorithm that uses a given memory limit in a more effective way. Rather than treating all 3D points equally, it represents a small set of points with full appearance information and an additional, larger set of points with compressed information. This enables our approach to obtain a more complete scene representation without increasing the memory requirements, leading to a superior performance compared to previous compression schemes. As part of our contribution, we show how to handle ambiguous matches arising from point compression during RANSAC. Besides outperforming previous compression techniques in terms of pose accuracy under the same memory constraints, our compression scheme itself is also more efficient. Furthermore, the localization rates and accuracy obtained with our approach are comparable to state-of-the-art feature-based methods, while using a small fraction of the memory.

visual localization

map compression


Federico Camposeco

Swiss Federal Institute of Technology in Zürich (ETH)

Andrea Cohen

Swiss Federal Institute of Technology in Zürich (ETH)

Marc Pollefeys

Microsoft Mixed Reality & AI Lab - Zürich

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

978-1-7281-3293-8 (ISBN)

IEEE / CVF Conference on Computer Vision and Pattern Recognition
Long Beach, USA,

Subject Categories


Computer Vision and Robotics (Autonomous Systems)



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