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Proceedings Paper

Surface reconstruction for 3D remote sensing
Author(s): Matthew S. Baran; Richard L. Tutwiler; Donald J. Natale
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Paper Abstract

This paper examines the performance of the local level set method on the surface reconstruction problem for unorganized point clouds in three dimensions. Many laser-ranging, stereo, and structured light devices produce three dimensional information in the form of unorganized point clouds. The point clouds are sampled from surfaces embedded in R3 from the viewpoint of a camera focal plane or laser receiver. The reconstruction of these objects in the form of a triangulated geometric surface is an important step in computer vision and image processing. The local level set method uses a Hamilton-Jacobi partial differential equation to describe the motion of an implicit surface in threespace. An initial surface which encloses the data is allowed to move until it becomes a smooth fit of the unorganized point data. A 3D point cloud test suite was assembled from publicly available laser-scanned object databases. The test suite exhibits nonuniform sampling rates and various noise characteristics to challenge the surface reconstruction algorithm. Quantitative metrics are introduced to capture the accuracy and efficiency of surface reconstruction on the degraded data. The results characterize the robustness of the level set method for surface reconstruction as applied to 3D remote sensing.

Paper Details

Date Published: 7 May 2012
PDF: 15 pages
Proc. SPIE 8399, Visual Information Processing XXI, 83990M (7 May 2012); doi: 10.1117/12.919257
Show Author Affiliations
Matthew S. Baran, The Pennsylvania State Univ. (United States)
Richard L. Tutwiler, The Pennsylvania State Univ. (United States)
Donald J. Natale, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)

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