2019.8.16. Site open.
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 is 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, Ignacio Rocco, Jiri Sedlar, Masatoshi Okutomi, Josef Sivic, Tomas Pajdla, Torsten Sattler, Akihiko Torii. Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization. In Proc. ICCV2019. (To appear) [bibtex] [ArXiv preprint]
Will be available soon.
This work was partially supported by JSPS KAKENHI Grant Numbers 15H05313, 17H00744, 17J05908, EU-H2020 project LADIO No. 731970, ERC grant LEAP No. 336845, CIFAR Learning in Machines & Brains program and the EU Structural and Investment Funds, Operational Programe Research, Development and Education under the project IMPACT (reg. no. CZ$.02.1.01/0.0/0.0/15_003/0000468$).
Please send bug reports and suggestions to Hajime Taira (htaira(at)ok.ctrl.titech.ac.jp)