3D Point Cloud Smoothing via Local Error Bar Adjustment
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Solution Overview
Problem
3D laser scanners produce point cloud scan files with noise and scatter points that distort the true dimensions of objects, making accurate measurements challenging, especially for small features and structural integrity analysis.
Innovation Solution
A method for processing point cloud data by calculating local error limits, adjusting distance values, and applying weighted averaging to reduce noise and scatter points, 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 scan data, then noise and scatter points are reduced, 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. Error bars are calculated locally for each pixel, and smoothing operations are selectively applied only where noise is detected, preserving the original data density in regions that do not require smoothing while reducing noise in problematic areas.
Solution Approach 2:
The patent modifies the error bar parameter dynamically during iterative processing. The error bar is initially calculated from the point cloud data and is updated in each iteration based on the smoothing results, allowing the processing parameters to adapt and optimize the balance between noise reduction and data density preservation.
2Measurement precision
If conventional smoothing methods are used, then statistical noise is reduced, but the true shape of the object is distorted
Solution Approach 1:
The patent implements an iterative feedback mechanism where the error bar is recalculated in each iteration based on the current state of the point cloud data. The smoothing operation uses this updated error bar to determine the appropriate level of filtering, and the process repeats until convergence or maximum iterations are reached, ensuring that the true shape is preserved while noise is reduced.
Solution Approach 2:
The patent employs dynamic error bar adjustment during the smoothing process. Rather than using a fixed smoothing parameter, the error bar is dynamically updated based on the local variance and distribution of points, allowing the smoothing intensity to adapt to the local geometry and preserve sharp features while reducing noise in flat regions.
3Measurement precision
If laser scanner settings are tuned to reduce noise, then measurement accuracy improves, but the field of view and coverage are reduced
Solution Approach 1:
The patent performs preliminary error bar calculation and noise characterization on the entire point cloud data set before applying smoothing operations. This allows the system to identify regions with high noise levels and apply targeted smoothing only where necessary, preserving the full field of view and data coverage while improving measurement accuracy in problematic areas.
Data Source
AI summary
A method and apparatus for processing an array of pixels in a point cloud, comprises calculating local error limits for each distance value for each pixel in the processed point cloud data set. One may then determine the error bar. One begins a distance value adjusting loop by for each pixel in the processed point cloud data set by calculating the difference between the distance value in the pixel of the point cloud data set being processed and each of the neighboring pixels or the most suitable neighboring pixel distance value is determined whether or not the difference is within the range defined by the error bar. The number of neighboring pixels with their distance values within the error bar for the pixel being processed is counted.