LiDAR Point Cloud Filling for Missing Pixels and Edge Preservation
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Solution Overview
Problem
LiDAR systems suffer from low accuracy in point cloud filling due to environmental noise and noise interference, leading to missing points in the point cloud data, which affects recognition accuracy.
Innovation Solution
A point cloud filling method that determines the position of missing pixels in LiDAR data, calculates a point filling mark based on smoothing and concentration features, and fills the missing pixels with accurate distance and reflectivity values to maintain object edge features and improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud filling is performed on all missing points, then the completeness of point cloud data is improved, but the accuracy of filling decreases due to noise interference and environmental factors
Solution Approach 1:
The patent applies local quality by differentiating between different types of missing points and applying different filling strategies. Points on object surfaces are filled using surface interpolation, while points in empty spaces are left unfilled. This selective approach ensures that filling operations are performed only where they improve completeness without compromising accuracy.
Solution Approach 2:
The patent performs preliminary classification of missing points before filling. By first identifying whether a missing point lies on an object surface or in empty space, the system prepares appropriate filling strategies in advance, preventing inaccurate filling of points that should remain empty.
2Reliability
If traditional 3D point cloud filling methods are used, then the completeness of point cloud data is improved, but the complexity of the filling process increases and edge features may be deformed
Solution Approach 1:
The patent segments the point cloud filling process into distinct stages: identifying missing points, classifying their locations relative to object surfaces, and applying appropriate filling methods. This segmentation simplifies the overall complexity by breaking down the filling process into manageable, independent steps.
Solution Approach 2:
Instead of attempting to fill all missing points uniformly, the patent inverts the approach by selectively identifying and filling only those missing points that lie on object surfaces. This inversion of the traditional approach reduces complexity by avoiding unnecessary filling operations in empty spaces while preserving edge features.
3Productivity
If point cloud filling is performed without selective judgment, then the processing speed is improved, but the recognition accuracy decreases due to contour deformation
Solution Approach 1:
The patent performs preliminary classification of missing points to determine their location relative to object surfaces before executing filling operations. This preliminary action enables the system to quickly identify which points require filling, maintaining processing speed while ensuring that only appropriate points are filled, thus preserving recognition accuracy.
Solution Approach 2:
The patent applies local quality by using different filling approaches for different locations. Points on object surfaces are filled using surface interpolation to maintain edge features, while points in empty spaces are left unfilled. This localized approach ensures high recognition accuracy without sacrificing processing speed.
Data Source
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AI summary
The present application provides a point cloud filling method, device, equipment, and storage medium. The method includes: obtaining the point cloud data collected by the LiDAR and determining the point missing pixel position from the point cloud data; obtaining the point filling mark of the point missing pixel position; determining the point missing pixel position as the point filling pixel position; performing point cloud filling on the point filling pixel position according to the point filling mark. In this way, can effectively improve the accuracy of point cloud filling, maintain the edge features of the object being measured in the field of view, and not cause contour deformation of the object being measured due to point cloud filling, thereby improving the accuracy of LiDAR recognition.