A point cloud analysis reconstruction method and system based on a cloud CAD platform

By using point cloud analysis and reconstruction methods on a cloud CAD platform, and leveraging symbolic distance field prediction and boundary prediction neural networks to automatically reconstruct CAD models, the problem of low efficiency and poor accuracy in manual operations in existing technologies is solved, achieving efficient and accurate point cloud data conversion.

CN121810984BActive Publication Date: 2026-06-05SHANDONG HUAYUN 3D TECH CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
SHANDONG HUAYUN 3D TECH CO LTD
Filing Date
2026-03-10
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing point cloud data reconstruction methods rely on manual operation, which is inefficient, has poor accuracy and consistency, and is difficult to adapt to complex scenarios and automation needs.

Method used

A point cloud analysis and reconstruction method based on a cloud CAD platform is adopted. By receiving point cloud data files uploaded by users, the symbolic distance field prediction and boundary prediction neural networks are used to identify local boundary information. Combined with the voxel region growing algorithm, the surface region is divided and fitted to automatically reconstruct the CAD model.

Benefits of technology

It achieves fully automated processing from point cloud data to CAD models, reduces manual interaction, improves modeling efficiency and accuracy, ensures modeling consistency, and adapts to complex surfaces and large-scale point cloud data scenarios.

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Abstract

The application discloses a point cloud analysis and reconstruction method and system based on a cloud CAD platform, belongs to the technical field of point cloud reconstruction, and is used for solving the technical problems that the existing point cloud data reconstruction mode depends on manual operation, is low in efficiency, poor in precision and consistency, and difficult to adapt to complex scenes and automation requirements. The method comprises the following steps: receiving a point cloud data file and determining a processing mode; determining a corresponding irregular sampling region according to the processing mode, and performing signed distance field prediction on the point cloud data file in the irregular sampling region to obtain unsigned distance field and gradient information; generating a sliding window, identifying local boundary information in the sliding window through a boundary prediction neural network, and performing splicing to obtain global boundary information; dividing a face region of the point cloud data file according to the global boundary information to obtain different types of faces; performing targeted fitting processing on each type of face, and solving a boundary representation relationship between the faces to reconstruct a CAD model.
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