3D Point Cloud Alignment via 2D Projection Consistency
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Solution Overview
Problem
Current 3D point cloud matching methods are inefficient and inaccurate, particularly in large-scale applications, as they require manual alignment and lack standardized processes, leading to varying accuracy among individuals.
Innovation Solution
An information processing method that generates a 2D point cloud image from projecting 3D point clouds onto a horizontal plane and determines projection coordinates based on the consistency degree with a reference plane graph, enabling automatic alignment of 3D points with reference coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual alignment methods are used for 3D point cloud matching, then flexibility in handling different cases is maintained, but matching efficiency and accuracy deteriorate due to lack of standardization and high manual effort
Solution Approach 1:
The patent replaces manual mechanical alignment operations with an automated computer-based system that performs coordinate system transformations and point cloud matching algorithms, eliminating the need for manual intervention while standardizing the process
Solution Approach 2:
The patent transforms the alignment problem by changing the coordinate system parameters through mathematical transformations (rotation, translation, scaling) to automatically align the 3D point cloud with the reference plane graph, replacing manual parameter adjustment with automated computational methods
2Measurement precision
If manual alignment processes are used, then adaptability to various scenarios is maintained, but measurement precision and consistency deteriorate due to varying individual accuracy
Solution Approach 1:
The patent replaces manual measurement and alignment operations with automated computational algorithms that consistently apply mathematical transformations, eliminating human error and variability while maintaining high precision across all matching operations
Solution Approach 2:
The patent creates a digital replica of the alignment process through computer-based coordinate transformations, allowing the same precise mathematical operations to be repeatedly applied without degradation in accuracy or increase in time consumption
3Adaptability or versatility
If 3D point cloud coordinates are defined in image acquiring device coordinate system, then the data structure remains simple, but geographical significance and applicability to real-world positioning deteriorate
Solution Approach 1:
The patent replaces manual coordinate system interpretation with automated coordinate transformation algorithms that convert device-specific coordinates into geographically meaningful coordinates through mathematical operations, enabling real-world positioning applications
Solution Approach 2:
The patent transforms the coordinate system by introducing dimensional transformations that map three-dimensional point cloud data into a two-dimensional reference plane coordinate system, adding geographical significance while maintaining computational efficiency
Data Source
AI summary
An information processing method includes: three-dimensional (3D) point information of a 3D point cloud is obtained; a two-dimensional (2D) point cloud image from projection of the 3D point cloud on a horizontal plane is generated based on the 3D point information; and projection coordinates of 3D points comprised in the 3D point cloud in a reference coordinate system of a reference plane graph are determined based on a consistency degree that the 2D point cloud image has with the reference plane graph, where the reference plane graph is used for representing a projection graph with reference coordinates that is obtained through the projection of a target object on the horizontal plane, and the 3D point cloud is used for representing 3D space information of the target object.