Author(s)

Yu Dali

Corresponding Author

Yu Dali

ABSTRACT

Advanced 3D scanning technologies enable us to obtain dense and accurate surface sample point sets. From sufficiently dense sample point set, Crust algorithm, which is based on Voronoi diagram and its dual Delaunay triangulation, can reconstruct a triangle mesh that is topologically valid and convergent to the original surface. However, the algorithm is restricted in the practical application because of its long running time, and when the point cloud must not be noisy, the surface reconstructed is not good. Surfaces are often reconstructed from unorganized point sets with noise, so denoising is an essential step in creating perfect point-sampled models. A novel surface reconstruction algorithm is proposed. Firstly, this paper determines if one point is the noise or not by the ellipsoid criterion. After acquiring new point sets being less noisy, we smooth the remains noise by mean shift point clouds denoising method. Experiments show that our method can smooth the noise efficiently. Secondly, a non-uniformly sampling method is used to resample the input data set according to the local feature size before reconstruction. Finally, the surface is reconstructed by crust algorithm. In this way, the speed of reconstruction is increased for noisy points without losing the details we need.

KEYWORDS

Surface Reconstruction, Ellipsoid Criterion, Down Sampling, Algorithm