1 code implementation • 3 Mar 2022 • Zijie Jiang, Hajime Taira, Naoyuki Miyashita, Masatoshi Okutomi
In this paper, we investigate the effect of different fusion strategies for ego-motion estimation and propose a new framework for self-supervised learning of depth and ego-motion estimation, which performs ego-motion estimation by leveraging RGB and inferred depth information in a Multi-Layer Fusion manner.
no code implementations • 7 Jul 2021 • Hajime Taira, Koki Onbe, Naoyuki Miyashita, Masatoshi Okutomi
In this paper we introduce a new camera localization strategy designed for image sequences captured in challenging industrial situations such as industrial parts inspection.
no code implementations • 24 Jan 2021 • Zijie Jiang, Hajime Taira, Naoyuki Miyashita, Masatoshi Okutomi
In this paper, we present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments by leveraging the camera pose information from a visual-inertial odometry.
no code implementations • 18 Jun 2020 • Sho kagami, Hajime Taira, Naoyuki Miyashita, Akihiko Torii, Masatoshi Okutomi
Pipe inspection is a critical task for many industries and infrastructure of a city.
no code implementations • ICCV 2019 • Hajime Taira, Ignacio Rocco, Jiri Sedlar, Masatoshi Okutomi, Josef Sivic, Tomas Pajdla, Torsten Sattler, Akihiko Torii
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.
1 code implementation • CVPR 2018 • Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map.
no code implementations • CVPR 2017 • Torsten Sattler, Akihiko Torii, Josef Sivic, Marc Pollefeys, Hajime Taira, Masatoshi Okutomi, Tomas Pajdla
3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately.