Adaptive grid fitting point cloud filtering method and system based on gradient compensation

By employing adaptive mesh fitting and gradient compensation methods, the problems of noise identification and feature extraction in point cloud data processing in existing technologies are solved. An adaptive mesh fitting method for point cloud data is realized, and the adaptability problem of existing mesh filtering methods is resolved, achieving efficient denoising and feature preservation of point cloud data.

CN121767228BActive Publication Date: 2026-06-26北京捷翔天地信息技术有限公司
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Patent Information

Application Number
CN202610250631.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-03
Publication Date
2026-06-26
Estimated Expiration
2046-03-03

AI Technical Summary

Technical Problem

Existing mesh filtering methods cannot accurately distinguish between noise and valid points when processing complex 3D point cloud data, resulting in feature loss or noise residue. Furthermore, discontinuities exist at mesh boundaries, affecting the accuracy of 3D reconstruction and feature recognition.

Method used

By acquiring the local density distribution characteristics of point cloud data to divide the grid, performing local surface fitting, calculating the gradient change and surface smoothness constraints, determining the gradient compensation amount, correcting the fitted surface parameters, and dynamically adjusting the filtering threshold by combining local curvature characteristics and deviation values, adaptive filtering is achieved.

Benefits of technology

It improves the accuracy and robustness of point cloud filtering, effectively removes noise while preserving geometric features, adapts to point cloud data with different densities and complex geometries, and outputs high-quality filtered point cloud data.

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Abstract

The present application relates to the technical field of point cloud data processing, and more particularly to a self-adaptive grid fitting point cloud filtering method and system based on gradient compensation. The method divides the grid by the local density of the point cloud and performs surface fitting, calculates the grid gradient variation to determine the gradient compensation, and then corrects the fitting surface parameters. Based on the corrected parameters, the deviation of each point from the fitting surface and the local curvature are calculated to determine the adaptive filtering threshold, thereby classifying and removing the noise points. The present application can effectively maintain the detailed features of the point cloud and improve the filtering robustness of complex surfaces and noise.
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