Adaptive 3D Scanner Equal-Area Point Cloud Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional 3D scanning techniques using fixed angular resolution result in over-representation and under-representation of points, leading to inefficient scanning, large output files, and the need for rescanning, especially in scenarios where optimal scanner positioning is restricted or impossible.
Innovation Solution
An adaptive equal-area scanning method that dynamically adjusts the scanning pattern based on the geometric nature of the surface, using a LiDAR scanner with driver software for generating equal-area density point clouds, low mass front-surface mirrors, and dynamic micro-stepping to achieve high servo speed and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed angular resolution scanning pattern is used, then the scanning process is simple and fast, but the point distribution becomes uneven with over-representation of close surfaces and under-representation of far surfaces
Solution Approach 1:
The patent implements dynamic scanning patterns that adapt in real-time based on measured surface geometry. The system continuously adjusts scanning parameters such as angular resolution and point spacing according to the actual distance and orientation of surfaces being scanned, transforming a static fixed-pattern approach into a dynamic adaptive one that maintains uniform point distribution while preserving scanning efficiency
Solution Approach 2:
The system dynamically modifies scanning parameters including angular step size, point density, and scan area based on real-time feedback from initial scans. By changing these parameters adaptively rather than using fixed values, the system achieves uniform point representation across surfaces at varying distances without sacrificing overall scanning speed
2Manufacturing precision
If the angular resolution is increased to cover underrepresented areas, then the point coverage improves, but the output file size increases significantly and scanning time increases
Solution Approach 1:
The patent applies different scanning resolutions locally to different regions of the scan area based on their specific geometric characteristics. Surfaces that require higher resolution are scanned with increased angular resolution, while well-represented areas use lower resolution, achieving uniform overall coverage without uniformly increasing the number of points everywhere, thus controlling output file size
3Manufacturing precision
If the scanner is repositioned to an optimal position, then the point distribution becomes more uniform, but the setup time and complexity increase
Solution Approach 1:
The system performs self-adjustment by automatically analyzing the scanned geometry and recalculating optimal scanning patterns without requiring external intervention or manual repositioning. The adaptive algorithm processes the point cloud data in real-time and autonomously modifies subsequent scanning patterns to achieve uniform coverage, eliminating the need for complex manual setup adjustments
4Productivity
If a fixed scanning pattern is used without geometric feedback, then the scanning process is efficient and fast, but the system cannot adapt to unknown or varying surface geometries
Solution Approach 1:
The patent implements a feedback loop where initial scan data is analyzed to determine actual surface geometry, and this information feeds back into dynamic adjustment of the scanning pattern. The system uses the measured point cloud to inform subsequent scanning decisions, creating a closed-loop adaptive process that maintains efficiency while achieving geometric adaptability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the number of representative points by 10-15 times, speeds up scanning time, eliminates the need for rescanning, and significantly reduces output file size while ensuring even point distribution, making it suitable for applications with limited or no prior knowledge of surface geometry.
Implementation Method 1
detecting at least a portion of the light scattered by the surface and determining, as a function of the detected light, a distance between the light source and the surface
Implementation Method 2
a 3D LiDAR (light detection and ranging) scanner embodying aspects of the present disclosure
Data Source
AI summary
An adaptive three-dimensional (3D) scanner having a light source configured to transmit light onto a surface according to a scanning pattern. A detector receives at least a portion of the light scattered by the surface. A processor is configured to determine, as a function of the received light, a distance between the light source and the surface and dynamically vary during scanning an angle at which the light is transmitted relative to a predefined coordinate system. In this manner, the processor repeatedly defines adjacent scan areas on the surface that are substantially equal in size.