Rapid normal vector orientation method for scattered point cloud

Abstract

The invention provides a rapid normal vector orientation method for scattered point cloud. The method comprises the following steps: initializing the normal vector orientation data structure of the scattered point cloud; selecting normal vector orientation seed points; and selecting a region growth direction based on the priority queue and adjusting a normal vector direction. According to the method, the rapid normal vector orientation of the scattered point cloud can be realized by combining a priority queue structure and a priority strategy with a region growing principle, and the problems of low orientation speed and non-robust orientation to singular conditions of a traditional scattered point cloud normal vector orientation method are solved.

Description

technical field [0001] The invention belongs to the technical field of computer graphics, and relates to a random point cloud normal vector orientation method. Background technique [0002] In recent years, point cloud data has been widely used in many fields such as industrial inspection, reverse engineering, human body scanning, cultural relic protection, computer games, digital movies, and physical simulation. Point-based models have become an important direction in the field of computer graphics. The normal vector of the point cloud model is the basis for data processing such as point cloud model drawing, smoothing, and surface reconstruction. It is very important to correctly estimate the normal vector of the point cloud. [0003] At present, the "plane method" is mostly used to estimate the normal vector of point cloud, that is, the plane is fitted by the neighboring points, and the normal vector of the plane is used to approximate the normal vector of the point. How...

AI Technical Summary

Problems solved by technology

The first type of method, the minimum spanning tree method, is the most widely used method, but it is prone to errors when there are singularities (thin walls, verticals, adjacent surfaces, etc.) in the point cloud.

There are many improved algorithms based on this method to solve the orientation errors in singular situations, but they still cannot solve the most essential problem - low computational efficiency and long time-consuming, so they cannot be put into actual production

The second type of photometric stereo vision method needs to use quite a lot of prior knowledge, which is greatly affected by the outside world and has poor stability.

The third method is the surface reconstruction method, which can correctly adjust the point cloud of the singular situation, but the calculation efficiency is lower than the minimum spanning tree, and it takes more time.

[0006] 1. In the case of point cloud singularity, the normal vector orientation is prone to problems, and the targeted improvement method is complex, computationally intensive and not robust;

[0007] 2. Traditional methods are inefficient and time-consuming, and cannot be used in actual production work

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