24/7 place recognition by view synthesis

Akihiko Torii (1) , Relja Arandjelovic (2, 3) , Josef Sivic (3, 2) , Masatoshi Okutomi (1) , Tomas Pajdla (4)

1 TITECH - Tokyo Institute of Technology [Tokyo]
2 LIENS - Laboratoire d'informatique de l'école normale supérieure
3 WILLOW - Models of visual object recognition and scene understanding
4 Department of Cybernetics [Prague]

We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact in-dexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly out-performs other large-scale place recognition techniques on this challenging data.

Domains: Computer Vision and Pattern Recognition [cs.CV]

Akihiko Torii, Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla. 24/7 place recognition by view synthesis. CVPR 2015 - 28th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2015, Boston, United States. ⟨hal-01147212⟩