Point Cloud Noise Reduction via Local Error Bar Processing
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Solution Overview
Problem
3D laser scanner point cloud files often contain noise such as statistical noise and false scatter points, which distort the true dimensions of objects and hinder accurate measurements.
Innovation Solution
A method for processing point cloud data by calculating local error limits for each pixel, adjusting distance values based on differences with neighboring pixels, and applying weighted averaging to reduce noise, while maintaining the original density of the scan data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smoothing techniques are applied to 3D point cloud data, then measurement accuracy is improved, but the density of the original scan data is distorted or diminished
Solution Approach 1:
The patent applies different processing treatments to different regions of the point cloud data based on local characteristics. Pixels are classified into different types (edge pixels, corner pixels, interior pixels) and each type receives appropriate processing. This local differentiation allows smoothing to be applied where beneficial while preserving data density where critical, resolving the contradiction between measurement accuracy and data density preservation.
2Reliability
If noise reduction is applied to remove statistical noise and scatter points, then measurement reliability is improved, but the original scan data is distorted
Solution Approach 1:
The patent changes the state of distance values through controlled adjustment processes. By calculating adjusted distance values based on neighboring pixels and applying fractional changes, the system reduces noise while maintaining the essential characteristics of the original data. The iterative nature of the process allows gradual convergence toward a reliable measurement without abrupt distortions.
3Measurement precision
If tools are used to measure between scan points, then measurement capability is improved, but the field of view is reduced and measurements deviate significantly
Solution Approach 1:
The patent enables the point cloud data itself to provide measurement information through direct processing of the distance values. By calculating adjusted distance values and determining surface characteristics directly from the processed data, the system eliminates the need for additional measurement tools that would restrict the field of view. The data serves its own measurement purpose while maintaining full coverage.
Data Source
AI summary
A method is explained for processing an array of pixels in a point cloud. Local error bars are calculated for each pixel distance value. A difference is calculated between distance values of the pixel being processed and neighboring pixels with distance values within the error bars. If the difference is outside the error bars, the distance value of the pixel being processed is changed by a small fraction while remaining inside the error bars; if the difference is within the error bars, the pixel value is replaced by a weighted average. The neighboring pixels with distance values within the error bars of the pixel are counted and if a predetermined threshold is met, the counted values are averaged and replace the pixel value, but if not met, the pixel value is unchanged. If loop exit criteria have been met, the loop is terminated and if not, looping begins again.