Feature sensitive re-sampling of point set surfaces with Gaussian spheres

Abstract

Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification. Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner. The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme. A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme. Moreover, a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.

Feature-preserving simplification framework for 3D point cloud

“…To evaluate the performance of the proposed framework, the simplified results of our method are compared with the results of the other six simplified methods: DFPSA 34 , Gaussian spheres 35 , octree coding 36 , k-means clustering 37 , uniform simplification 38 , and geometric algebra 39 . The experiment uses two platforms.…”