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Proceedings Paper

A computationally efficient approach to 3D point cloud reconstruction
Author(s): C.-H. Chang; N. Kehtarnavaz; K. Raghuram; R. Staszewski
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Paper Abstract

This paper addresses improving the computational efficiency of the 3D point cloud reconstruction pipeline using uncalibrated image sequences. In existing pipelines, the bundle adjustment is carried out globally, which is quite time consuming since the computational complexity keeps growing as the number of image frames is increased. Furthermore, a searching and sorting algorithm needs to be used in order to store feature points and 3D locations. In order to reduce the computational complexity of the 3D point cloud reconstruction pipeline, a local refinement approach is introduced in this paper. The results obtained indicate that the introduced local refinement improves the computational efficiency as compared to the global bundle adjustment.

Paper Details

Date Published: 19 February 2013
PDF: 8 pages
Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 86560O (19 February 2013); doi: 10.1117/12.2000483
Show Author Affiliations
C.-H. Chang, The Univ. of Texas at Dallas (United States)
N. Kehtarnavaz, The Univ. of Texas at Dallas (United States)
K. Raghuram, Texas Instruments Inc. (United States)
R. Staszewski, Texas Instruments Inc. (United States)

Published in SPIE Proceedings Vol. 8656:
Real-Time Image and Video Processing 2013
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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