Spectral Surface Reconstruction From Noisy Point Clouds

: We introduce a noise-resistant algorithm for reconstructing a watertight surface from point cloud data. It forms a Delaunay tetrahedralization, then uses a variant of spectral graph partitioning to decide whether each tetrahedron is inside or outside the original object. The reconstructed surface triangulation is the set of triangular faces where inside and outside tetrahedra meet. Because the spectral partitioner makes local decisions based on a global view of the model, it can ignore outliers, patch holes and undersampled regions, and surmount ambiguity due to measurement errors. Our algorithm can optionally produce a manifold surface. We present empirical evidence that our implementation is substantially more robust than several closely related surface reconstruction programs. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computing Methodologies]: Computer Graphics Computational Geometry and Object Modeling

• Type: Conference
• Year: 2004
• Where: SGP
• Authors: Ravi Krishna Kolluri, Jonathan Richard Shewchuk, James F. O'Brien
• Keywords: SGP 2004, SGP 2007, Surface Reconstruction Programs, Surface Triangulation, Watertight Surface