Is this the right place? geometric-semantic pose verification for indoor visual localization
Paper in proceeding, 2019

Visual localization in large and complex indoor scenes, dominated by weakly textured rooms and repeating geometric patterns, is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics. To handle the ambiguities arising in this scenario, a common strategy is, first, to generate multiple estimates for the camera pose from which a given query image was taken. The pose with the largest geometric consistency with the query image, e.g., in the form of an inlier count, is then selected in a second stage. While a significant amount of research has concentrated on the first stage, there has been considerably less work on the second stage. In this paper, we thus focus on pose verification. We show that combining different modalities, namely appearance, geometry, and semantics, considerably boosts pose verification and consequently pose accuracy. We develop multiple hand-crafted as well as a trainable approach to join into the geometric-semantic verification and show significant improvements over state-of-the-art on a very challenging indoor dataset.


Hajime Taira

Tokyo Institute of Technology

Ignacio Rocco

Institut National de Recherche en Informatique et en Automatique (INRIA)

Jiri Sedlar

Czech Technical University in Prague

Masatoshi Okutomi

Tokyo Institute of Technology

Josef Sivic

Institut National de Recherche en Informatique et en Automatique (INRIA)

Czech Technical University in Prague

Tomas Pajdla

Czech Technical University in Prague

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Akihiko Torii

Tokyo Institute of Technology

Proceedings of the IEEE International Conference on Computer Vision

15505499 (ISSN)

Vol. 2019-October 4372-4382 9008526
978-172814803-8 (ISBN)

17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Seoul, South Korea,

Subject Categories

Computer Vision and Robotics (Autonomous Systems)



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