A 3D Surface Reconstruction Method Based on Delaunay Triangulation

Abstract

Point cloud models acquired by passive three-dimensional reconstruction systems based on binocular or multi-view involve large amounts of noise and its distribution is uneven, which affects the accuracy of surface reconstruction. To tackle the problem, we proposed a three-dimensional surface reconstruction method based on Delaunay triangulation. First, use Delaunay triangulation to get a fully adaptive decomposition of point cloud, then the output triangular mesh was represented using dual graph, so by using graph cut optimization the initial surface model was obtained. Second, the deformation model was used to optimize the initial surface model, and then adopted photometric similarity function and Laplace operator to refine surface details. Finally, the refinement was transformed into an iterative procedure, by which the real surface of the object was accurately approximated. We experimented on four standard datasets of Castle-Entry, Castle, Fountain and Herzjesu. The result showed that compared with Poisson surface reconstruction and floating scale surface reconstruction algorithm, the proposed method was more adaptable and robust to noise and outliers, and the detailed information recovery of local surface reconstruction was better. It showed that the method proposed in this paper effectively improved the accuracy and completeness of surface reconstruction, which could reconstruct high quality three-dimensional surface models of object from the point cloud models with lots of noise and complex topological structures.