Reconstructing the surface of unstructured 3D data
María-Elena Algorri, Francis Schmitt · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Building 3D models from unstructured data is a fundamental problem that arises increasingly in the medical field as new 3D scanning technology is able to produce large and complex databases of full 3D information. In addition, the huge efforts put into segmenting entire sets of 2D images demand robust tools that are then able to reconstruct any arbitrary 3D surface segmented from the images. In this paper we propose an algorithmic methodology that automatically produces a simplicial surface from a set of points in R3 about which we have no topological knowledge. Our method uses a spatial decomposition and a surface tracking algorithm to produce a rough approximation S' of the unknown manifold S. The produced surface S' serves as a robust initialization for a physically based modeling technique that incorporates the fine details of S and improves the quality of the reconstruction. The result of the reconstruction is a dense triangulation S' that undergoes a stage of mesh decimation to produce a compact representation of S.