Robust surface reconstruction from defective point clouds by using orientation inference and volumetric regularization

Surface reconstruction is a critical stage in the 3D data acquisition and model creation system. Most existing reconstruction algorithms are designed for oriented data, i.e. point sets with surface normals. However, in some applications, explicit orientation information may not be available, e.g. Shape from Contour (SfC). Besides, the point sets recovered from images and camera calibration are typically noisy and contains defects, e.g. holes or non-uniform sampling. We present a robust method that achieves smooth surface approximation from unoriented and defective point sets by orientation inference and volumetric regularization.