A Simplification Method for Cloud Points Based on Local Surface Fitting
HE Han-gen · Computer Engineering and Science · 2010
With the improvement of the technology of data acquirement,the cloud-point data is used more and more widely in 3D reconstruction.The huge data size becomes the bottleneck of reconstruction efficiency.The feature of models is blurred because of the calculation accuracy of the curvature used in the existing simplification methods.A quantitative definition of the surface feature is proposed based on qualitative analysis.The approximate surface near a sampled point is obtained by the local surface fitting method.Then the feature of the surface near the sampled point is described by the average of the normal curvature in 360 degree instead of the average curvature.A K-D tree partitioning method is adopted to segment the cloud points according to the surface feature,the size of space area and the size of sampling nodes.Experiments show that this method preserves the geometry feature of the surface better.This result demonstrates the efficiency of the method.