IEICE TRANSACTIONS on Information
Superimposing Thermal-Infrared Data on 3D Structure Reconstructed by RGB Visual Odometry
Summary :
In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.5 pp.1296-1307
- Publication Date
- 2018/05/01
- Publicized
- 2018/02/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017MVP0023
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision and its Applications)
- Category
- Machine Vision and its Applications
Authors
Masahiro YAMAGUCHI
Keio University
Trong Phuc TRUONG
Keio University
Shohei MORI
Keio University
Vincent NOZICK
Keio University,Universite Paris-Est Marne-la-Vallee
Hideo SAITO
Keio University
Shoji YACHIDA
NEC
Hideaki SATO
NEC
Keyword
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Masahiro YAMAGUCHI, Trong Phuc TRUONG, Shohei MORI, Vincent NOZICK, Hideo SAITO, Shoji YACHIDA, Hideaki SATO, "Superimposing Thermal-Infrared Data on 3D Structure Reconstructed by RGB Visual Odometry" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 5, pp. 1296-1307, May 2018, doi: 10.1587/transinf.2017MVP0023.
Abstract: In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017MVP0023/_p
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@ARTICLE{e101-d_5_1296,
author={Masahiro YAMAGUCHI, Trong Phuc TRUONG, Shohei MORI, Vincent NOZICK, Hideo SAITO, Shoji YACHIDA, Hideaki SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Superimposing Thermal-Infrared Data on 3D Structure Reconstructed by RGB Visual Odometry},
year={2018},
volume={E101-D},
number={5},
pages={1296-1307},
abstract={In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.},
keywords={},
doi={10.1587/transinf.2017MVP0023},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Superimposing Thermal-Infrared Data on 3D Structure Reconstructed by RGB Visual Odometry
T2 - IEICE TRANSACTIONS on Information
SP - 1296
EP - 1307
AU - Masahiro YAMAGUCHI
AU - Trong Phuc TRUONG
AU - Shohei MORI
AU - Vincent NOZICK
AU - Hideo SAITO
AU - Shoji YACHIDA
AU - Hideaki SATO
PY - 2018
DO - 10.1587/transinf.2017MVP0023
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2018
AB - In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.
ER -
English
