2D Regularized Point Cloud Projection Using Horizontal Azimuth
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Solution Overview
Problem
Existing methods for 2D regularized planar projection of large-scale point cloud data suffer from high algorithm complexity due to the need for local searches on every point, leading to excessive time consumption and affecting performance.
Innovation Solution
A method involving the calculation of horizontal azimuth information to determine a mapping relationship between the point cloud and a planar structure, reducing the need for 2D local searches by using formulas to derive row and column indices based on azimuth information, thereby projecting large-scale point clouds to a 2D regularized plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D local search is performed on every point in the point cloud to determine mapping relationship, then projection accuracy is improved, but algorithm complexity increases and time consumption becomes excessive
Solution Approach 1:
The patent transforms the mapping relationship determination from a spatial local search problem to a parameter calculation problem. By using horizontal azimuth information (angular parameter) to directly compute row and column indices, the method changes the problem parameters from spatial coordinates requiring search to angular parameters enabling direct calculation, thereby resolving the contradiction between accuracy and complexity
Solution Approach 2:
The patent replaces the mechanical iterative local search process with a direct mathematical calculation system. Instead of performing repeated spatial searches to find optimal matching pixels, the invention uses azimuth-based formulas to directly determine the mapping relationship, substituting a complex mechanical search process with a simpler computational approach that maintains accuracy while reducing complexity
2Measurement precision
If 2D local search is performed on every point in the point cloud, then mapping relationship is determined accurately, but time consumption becomes excessive
Solution Approach 1:
The patent performs preliminary calculation of horizontal azimuth information for each point before the mapping process. This pre-computed angular parameter serves as the basis for direct index calculation, eliminating the need for time-consuming local searches during the actual projection phase, thus resolving the time consumption issue while maintaining accuracy
Solution Approach 2:
The patent replaces the time-consuming iterative local search mechanism with a direct mathematical computation system based on azimuth angles. By substituting the search-based approach with formula-based calculation, the method dramatically reduces processing time while preserving mapping accuracy
Data Source
AI summary
The present invention discloses a method and an apparatus for 2D regularized planar projection of a point cloud. The method includes: obtaining original point cloud data; initializing a planar structure of 2D projection of the point cloud; calculating horizontal azimuth information of the point cloud based on the original point cloud data; and determining a mapping relationship between the original point cloud data and the planar structure of 2D projection based on the horizontal azimuth information, to obtain a planar structure of 2D regularized projection of the point cloud. In the present invention, a large-scale point cloud may be projected to a 2D regularized planar structure without 2D local search. Therefore, complexity of an algorithm can be reduced, time spent on 2D regularized planar projection of the point cloud can be reduced, and algorithm performance can be improved.