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Proceedings of CAD'14, 2014, 85-87
Functional Surface Reconstruction from Unorganized Noisy Point Clouds
Abstract. Nowadays, it becomes easily to obtain large and complex object models composed of point clouds sampled from real-world objects with the help of high precision 3D scanners. As a result, point-based techniques such as point rendering, parameterization, simplification, shape reconstruction become hot research topics in CAD, Computer Graphics and Reverse Engineering. So far, considerable works have been done to reconstruct surfaces from the point clouds. The main goal of this work is also to develop a new method for curved surface reconstruction directly from unorganized noisy point clouds, especially for functional surface reconstruction. Methods regarding the surface reconstruction from the unstructured point clouds have been proposed, such as Delaunay tetrahedralization, the level set method, radial basis function and compactly supported radial basis function. However, Delaunay tetrahedralization based methods may fail when dealing with noisy point clouds. The level set method is proved powerful for surface reconstruction but its implementation is expensive in time and memory when high accuracy reconstruction is required. Although implicit surface reconstruction methods are attractive, finding a set of functions to form an implicit surface is difficult, especially for the free form surface. As a result, surface reconstruction from noisy point clouds is still an open question, let alone reconstructing the surface with regard to its function.
Keywords. Surface reconstruction, point cloud, feature detection, difference of normals