Surface Reconstruction for 3D Remote Sensing

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 three-space. 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.