Establishing a balanced neighborhood of discrete points for local quadric surface fitting. (March 2017) - Khameneifar, Farbod; Feng, Hsi-Yung
Abstract: This paper presents a novel algorithm to establish a balanced neighborhood of points for reliable local quadric surface fitting, a common task in point cloud data processing. The underlying smooth surface geometry of a point cloud in the vicinity of a point can be locally approximated by the best fitted quadric surface at the point. The quality of the fitted surface considerably depends on what neighboring points are selected for the fitting. Specifically, if the selected neighboring points carry a biased distribution, the fitted geometry becomes biased, resulting in loss of accuracy in the fitting. The presented algorithm in this paper is able to reliably select neighboring points considering measures of both distance and direction. The main feature is the development of a geometric relationship, named as Territory Claiming, between the selected and the candidate neighboring points. The fundamental principle is for the selected point set to cover the whole neighborhood domain without redundancy. The selection procedure starts with a distance-based sequence of neighboring points with the territory claiming relationship functioning as a filter to establish a well-balanced neighborhood. The neighborhood can be expanded to incorporate sufficient number of points for the quadric surface fitting while maintaining the balance of the overall neighborhood. The implementation results have demonstrated that the presented method is robust and selects local neighboring points with superior fitting performance in comparison with the distance-based neighbors, mesh neighbors, and elliptic Gabriel graph neighbors. Highlights: A novel method to identify the closest directionally balanced neighboring point set. Balanced neighborhood of points expandable to include more points. Effectiveness validated via comparison with existing methods.