3D image reconstruction is desirable in many applications such as city planning, cartography and many vision applications. The accuracy of the 3D reconstruction plays a vital role in many real world applications. We introduce a method which uses one LiDAR image and N conventional visual images to reduce the error and to build a robust registration for 3D reconstruction. In this method we used lines as features in both the LiDAR and visual images. Our proposed system consists of two steps. In the first step, we extract lines from the LiDAR and visual images using Hough transform. In the second step, we estimate the camera matrices using a search algorithm combined with the fundamental matrices for the visual cameras. We demonstrate our method on a synthetic model which is an idealized representation of an urban environment.

Date Published: 29 April 2013
PDF: 9 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 874808 (29 April 2013); doi: 10.1117/12.2016357

Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)