Incremental Mesh-based Integration of Registered Range Images: Robust to Registration Error and Scanning Noise

Abstract

Existing integration algorithms often assume that the registration error of neighbouring views is an order of magnitude less than the measurement error [3]. This assumption is very restrictive that automatic registration algorithms can hardly meet. In this paper, we develop a novel integration algorithm, robust to both large registration errors and heavy scanning noise. Firstly, a pre-processing procedure is developed to automatically triangulate a single range image and remove noisy triangles. Secondly, we shift points along their orientations by the projection of their resulting correspondence vectors so that new correspondences can approach together, leading large registration errors to be compensated. Thirdly, overlapping areas between neighbouring views are detected and integrated, considering the confidence of triangles, which is a function of the including angle between the centroid point vector of a triangle and its normal vector. The outcome of integration is a set of disconnected triangles where gaps are caused by the removal of overlapping triangles with low confidence. Fourthly, the disconnected triangles are connected based on the principle of maximizing interior angles. Since the created triangular mesh is not necessarily smooth, finally, we minimize the weighted orientation variation. The experimental results based on real images show that the proposed algorithm significantly outperforms an existing algorithm and is robust to both registration error and scanning noise.