Camera pose estimation for mixed and diminished reality in FTV

Hideo Saito; Toshihiro Honda; Yusuke Nakayama; Francois De Sorbier

doi:10.1109/3DTV.2014.6874756

Camera pose estimation for mixed and diminished reality in FTV

Hideo Saito, Toshihiro Honda, Yusuke Nakayama, Francois De Sorbier

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In this paper, we will present methods for camera pose estimation for mixed and diminished reality visualization in FTV application. We first present Viewpoint Generative Learning (VGL) based on 3D scene model reconstructed using multiple cameras including RGB-D camera. In VGL, a database of feature descriptors is generated for the 3D scene model to make the pose estimation robust to viewpoint change. Then we introduce an application of VGL to diminished reality. We also present our novel line feature descriptor, LEHF, which is also be applied to a line-based SLAM and improving camera pose estimation.

Original language	English
Title of host publication	3DTV-Conference
Subtitle of host publication	The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014
Publisher	IEEE Computer Society
ISBN (Print)	9781479947584
DOIs	https://doi.org/10.1109/3DTV.2014.6874756
Publication status	Published - 2014
Event	3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014 - Budapest, Hungary Duration: 2014 Jul 2 → 2014 Jul 4

Publication series

Name	3DTV-Conference
ISSN (Print)	2161-2021
ISSN (Electronic)	2161-203X

Other

Other	3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014
Country	Hungary
City	Budapest
Period	14/7/2 → 14/7/4

Keywords

augmented reality
camera calibration
feature descriptor
free viewpoint image synthesis
see-through vision

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Networks and Communications
Computer Vision and Pattern Recognition
Human-Computer Interaction
Electrical and Electronic Engineering

Access to Document

10.1109/3DTV.2014.6874756

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Cite this

Saito, H., Honda, T., Nakayama, Y., & De Sorbier, F. (2014). Camera pose estimation for mixed and diminished reality in FTV. In 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014 [6874756] (3DTV-Conference). IEEE Computer Society. https://doi.org/10.1109/3DTV.2014.6874756

Camera pose estimation for mixed and diminished reality in FTV. / Saito, Hideo; Honda, Toshihiro; Nakayama, Yusuke; De Sorbier, Francois.

3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014. IEEE Computer Society, 2014. 6874756 (3DTV-Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saito, H, Honda, T, Nakayama, Y & De Sorbier, F 2014, Camera pose estimation for mixed and diminished reality in FTV. in 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014., 6874756, 3DTV-Conference, IEEE Computer Society, 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2014, Budapest, Hungary, 14/7/2. https://doi.org/10.1109/3DTV.2014.6874756

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abstract = "In this paper, we will present methods for camera pose estimation for mixed and diminished reality visualization in FTV application. We first present Viewpoint Generative Learning (VGL) based on 3D scene model reconstructed using multiple cameras including RGB-D camera. In VGL, a database of feature descriptors is generated for the 3D scene model to make the pose estimation robust to viewpoint change. Then we introduce an application of VGL to diminished reality. We also present our novel line feature descriptor, LEHF, which is also be applied to a line-based SLAM and improving camera pose estimation.",

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