Adaptive 3D mesh reconstruction from dense unorganized weighted points using neural network

The reconstruction of a shape or surface from unorganized points is a practically significant and theoretically challenging problem. In this paper, we present an efficient and uniform approach for the automatic reconstruction of surfaces of CAD (computer aided design) models and scalar fields defined on them, from an unorganized collection of scanned point data using neural networks. In comparison with the traditional methods, examples show that the algorithm is verified to be accurate and applicable. Compared with earlier methods our algorithm has the advantages of simplicity, efficiency and uniformity (both CAD model and scalar field reconstruction).