Surface reconstruction for 3D remote sensing
2012
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 R 3 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.
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