An efficient method for surface reconstruction based on local coordinate system transform and partition of unity

Radial basis function (RBF) has been extensively applied for surface reconstruction from scattered 3D point data due to its strong ability of approximation. However, additional information, such as off-surface points, are usually required to be appended into constraints for determining the parameters, which apparently increases the computation cost and data unreliability. To avoid adding additional off surface point constraints, a novel surface reconstruction approach based on local coordinate system transform and partition of unity is proposed in this paper. Firstly, the explicit RBF functions are constructed to approximate the local surface patches, and then it is transformed into an equivalent implicit surface reconstruction form by local system coordinate transformation. Compared with the local implicit surface approximation, the proposed local explicit surface approximation method is capable of avoiding trivial solution occurred in RBF approximating, and does not increase the scale of data solution. A number of comparison experiments of the proposed method with the traditional RBF-based method and the multi-level partition of unity (MPU) method are carried out on some kinds of large dataset, non-uniformity dataset, noisy dataset. The experimental results illustrate that the proposed method is robust and effective in dealing with large-scale point clouds surface reconstruction.