Detection of Holes in Point Clouds Using Statistical Technique

Zain ul Abideen; Hamza Ali; Muhammad Sajjad; Muhammad Abeer Irfan; Atif Jan; Yasir Saleem

Authors

Zain ul Abideen Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
Hamza Ali Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
Muhammad Sajjad Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
Muhammad Abeer Irfan Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
Atif Jan Department of Electrical Engineering University of Engineering and Technology, Peshawar, Pakistan,
Yasir Saleem Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan

Keywords:

Point clouds, Hole detection, Surface reconstruction, Statistical analysis,

Abstract

A point cloud is a dynamic, three-dimensional geometric representation of data that has different qualities for every point, including geometry, normal vectors, and color. However, holes that often occur during the 3D point cloud collection process provide an immense obstruction to the analysis and reconstruction of point clouds. Thus, detecting these holes is a crucial initial step toward obtaining precise and comprehensive representations of the real surfaces. Although there are several methods available for hole detection and filling, the problem is exacerbated by their shortcomings, which include high computation complexity or limited effectiveness. Our method is based on a sequence of basic but efficient statistical techniques. Our method is based on a sequence of basic but efficient statistical techniques. First, we find the mean distances between each point using the K Nearest Neighbors (KNN) technique. Next, we can categorize normal points and points that belong to holes and borders by using this mean as a threshold. Our method's simplicity and low computational resource needs offer significant advantages over other approaches.

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