Three-dimensional point cloud semantic segmentation and measurement reference surface intelligent fitting system and method thereof

Through the combination of multi-sensor fusion and deep learning, the problem of semantic segmentation and datum fitting in three-dimensional point cloud data is solved, and the datum fitting with high precision and high reliability is achieved, adapting to complex environments and reducing the dependence of labeled data.

CN120451540AInactive Publication Date: 2025-08-08GUANGDONG POLYTECHNIC COLLEGE
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Patent Information

Application Number
CN202510530062.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-25
Publication Date
2025-08-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the prior art, semantic segmentation and reference plane fitting and splitting of three-dimensional point cloud data lead to insufficient accuracy, especially in complex environments, it is difficult to accurately identify and fit reliable measurement reference planes, and it is highly dependent on labeled data.

Method used

Multi-sensor fusion is used to collect data, combine the organic fusion of deep learning and geometric processing, and extract features through PointNet++ network, mix RANSAC algorithm to fit semantic planes, and introduce a confidence evaluation mechanism, and use adversarial generation network to generate lithologic labels to achieve multi-scale feature output.

Benefits of technology

It improves the accuracy and reliability of datum fitting, reduces dependence on labeled data, adapts to complex and changeable environments, and has strong environmental adaptability and anti-interference ability.

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Abstract

The invention relates to the technical field of computer vision and three-dimensional measurement, in particular to a three-dimensional point cloud semantic segmentation and measurement reference surface intelligent fitting system and a method thereof, which are mainly applied to scenes needing accurate three-dimensional space understanding, such as topographic survey, mining, urban planning, intelligent driving and industrial detection. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a reference surface acquisition module and a pixel feature conversion module. Three-dimensional point cloud data are collected through multi-sensor fusion, and the data quality and integrity are improved. A cascade filtering preprocessing process is innovatively designed, and the data quality is remarkably improved. The feature extraction module generates semantic tags, the reference surface acquisition module calculates a probability optimal fitting plane, and the pixel feature conversion module realizes multi-scale feature output. According to the method, the three-dimensional point cloud processing precision and efficiency are effectively improved, and powerful technical support is provided for the field of computer vision and three-dimensional measurement.
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