2D Regularized Point Cloud Projection Without Local Search

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, leading to excessive time consumption and impaired performance.

Innovation Solution

A method and apparatus that utilize horizontal azimuth information to determine the mapping relationship between the point cloud and the planar structure, eliminating the need for 2D local searches by calculating row and column indices based on algebraic and geometric relationships with calibration parameters, thereby projecting large-scale point clouds to a 2D regularized plane.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If 2D local search is performed on every point in the point cloud to find optimal matching pixels, then mapping accuracy is improved, but algorithm complexity increases and processing time becomes excessive

Engineering Contradiction:

Improvemapping accuracyVSAvoidalgorithm complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent pre-calculates and stores the mapping relationship between 3D point cloud coordinates and 2D planar projection coordinates before actual processing. By establishing this mapping relationship in advance through coordinate transformation formulas, the system avoids performing time-consuming 2D local searches during the main processing stage, thus reducing algorithm complexity while maintaining mapping accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical 2D local search process with a mathematical coordinate transformation system. Instead of iteratively searching for optimal matching pixels in the 2D projection, the system uses pre-established transformation relationships to directly compute the corresponding 2D coordinates from 3D point cloud data, significantly reducing computational complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If 2D local search is performed on every point in the point cloud to find optimal matching pixels, then mapping accuracy is improved, but processing time becomes excessive

Engineering Contradiction:

Improvemapping accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent pre-calculates and stores the mapping relationship between 3D point cloud coordinates and 2D planar projection coordinates before actual processing. By establishing this mapping relationship in advance through coordinate transformation formulas, the system avoids performing time-consuming 2D local searches during the main processing stage, thus reducing algorithm complexity while maintaining mapping accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical 2D local search process with a mathematical coordinate transformation system. Instead of iteratively searching for optimal matching pixels in the 2D projection, the system uses pre-established transformation relationships to directly compute the corresponding 2D coordinates from 3D point cloud data, significantly reducing computational complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If traditional 2D projection methods are used, then point cloud data can be projected to 2D plane, but the representation form does not facilitate easy data processing due to sparsity and uneven distribution

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidrepresentation quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent transforms the point cloud data from its original 3D coordinate system to a 2D planar projection coordinate system through mathematical transformation. This parameter change in the coordinate system allows the point cloud to be represented in a 2D regularized projection that maintains spatial relationships while facilitating easier data processing and analysis.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent projects 3D point cloud data onto a 2D plane, effectively reducing the dimensionality from three dimensions to two dimensions. This dimensionality change simplifies the data structure while preserving the essential spatial correlation information, making the data more suitable for subsequent processing operations.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentEP4243412B1Two-dimensional regularized plane projection method and apparatus for point cloud

Publication Date: 2025.07.09 HONOR DEVICE CO LTD

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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, the mapping relationship between the original point cloud data and the planar structure of 2D projection is determined through the horizontal azimuth information of the point cloud, and 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.