InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Journal article, 2021

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor spaces. The method proceeds along three steps: (i) efficient retrieval of candidate poses that scales to large-scale environments, (ii) pose estimation using dense matching rather than sparse local features to deal with weakly textured indoor scenes, and (iii) pose verification by virtual view synthesis that is robust to significant changes in viewpoint, scene layout, and occlusion. Second, we release a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data. Code and data are publicly available.

feature matching

view synthesis

Visual localization

image retrieval

pose estimation

place recognition


Hajime Taira

Tokyo Institute of Technology

Masatoshi Okutomi

Tokyo Institute of Technology

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Mircea Cimpoi

Czech Technical University in Prague

Marc Pollefeys

Swiss Federal Institute of Technology in Zürich (ETH)

Josef Sivic

Czech Technical University in Prague

Tomas Pajdla

Czech Technical University in Prague

Akihiko Torii

Tokyo Institute of Technology

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN) 19393539 (eISSN)

Vol. 43 4 1293-1307 8894513

Subject Categories


Computer Science

Computer Vision and Robotics (Autonomous Systems)





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