Optimization Algorithm for Point Cloud Quality Enhancement Based on Statistical Filtering

Qian Zhao,Lin Luo,Xiaorong Gao,Jinlong Li,Rosalba Liguori

doi:10.1155/2021/7325600

Abstract

In the process of acquiring point cloud data by a 3D laser scanner, some problems, such as outliers, mixed points, and holes, may be caused in the target point cloud due to the external environment, the discreteness of the laser beam, and the occlusion of objects. In this paper, a point cloud quality optimization and enhancement algorithm is designed. A self-adaptive octree is established to rasterize the point cloud and calculate the density of each grid, combing with the statistical filtering to remove outliers from the point cloud data. Then, a plane projection method is used for removing the confounding points from the point cloud data. Finally, the point cloud is triangulated and a priority value is set, and then, points are preferentially inserted where the priority value is the largest to repair the holes. Experiments show that while removing outliers and confounding points, the detailed features of the point cloud can be maintained, holes are effectively filled, and the quality of the point cloud is effectively improved.

Highlights

In recent years, laser 3D scanning technology has developed rapidly
Four different filtering algorithms were well used for filtering the raw point cloud data form a UAV by Mustafa Zeybek [7], and a developed methodology contributes to the reduction of errors caused by data losses in various modelling studies [8]
The results show that the proposed algorithm in this paper preserves the sharp and detailed features of the point cloud while removing the outliers and confounding points and makes an effective repair for the holes

Summary

Introduction

Laser 3D scanning technology has developed rapidly. As a new format of data, the three-dimensional point cloud can accurately record the three-dimensional topography, geometric features, spatial coordinates, and other information about the surface of the object, and it has many advantages that two-dimensional data does not have [1]. Based on the locally optimal projection (LOP) algorithm proposed by Lipman et al [10], Huang et al [11] proposed weighted locally optimal projection algorithms that can remove some noise points during the projection These algorithms have poor robustness and cannot keep the sharp features of the point cloud well. Liu Zhenghong and Yun [23] extracted the boundary of the holes and determined the direction of contraction by calculating the direction of the triangular surface’s normal vector related to the boundary point In this way, a complete triangular patch is iteratively generated, but the number of additional points could not be controlled. By setting the priority value, the number of new points could be controlled when repairing holes

Theoretical Derivation

Experimental Results and Data Analysis

Conclusions