A computationally efficient approach to 3D point cloud reconstruction

C.-H. Chang; N. Kehtarnavaz; K. Raghuram; R. Staszewski

doi:10.1117/12.2000483

19 February 2013 A computationally efficient approach to 3D point cloud reconstruction

C.-H. Chang, N. Kehtarnavaz, K. Raghuram, R. Staszewski

Proceedings Volume 8656, Real-Time Image and Video Processing 2013; 86560O (2013) https://doi.org/10.1117/12.2000483
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

Citation Download Citation

C.-H. Chang, N. Kehtarnavaz, K. Raghuram, and R. Staszewski "A computationally efficient approach to 3D point cloud reconstruction", Proc. SPIE 8656, Real-Time Image and Video Processing 2013, 86560O (19 February 2013); https://doi.org/10.1117/12.2000483

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KEYWORDS

Cameras

3D image processing

Clouds

3D image reconstruction

Reconstruction algorithms

Stereoscopic cameras

3D displays

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