InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Artikel i vetenskaplig tidskrift, 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, Elektroteknik, Signalbehandling och medicinsk teknik

Mircea Cimpoi

Ceske Vysoke Uceni Technicke v Praze

Marc Pollefeys

Eidgenössische Technische Hochschule Zürich (ETH)

Josef Sivic

Ceske Vysoke Uceni Technicke v Praze

Tomas Pajdla

Ceske Vysoke Uceni Technicke v Praze

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


Robotteknik och automation

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)





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