A method and device for manufacturing a bridge real scene three-dimensional point cloud dataset and a medium

By constructing a point cloud dataset using bridge models and component parameters, this approach addresses the shortcomings of existing methods for acquiring 3D point cloud data of bridge real-world scenes. It generates an efficient and diverse dataset suitable for deep learning, resolving the discrepancy issue in simulating real scanning processes in existing technologies.

CN119478240BActive Publication Date: 2025-11-04SOUTHWEST JIAOTONG UNIV +1
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Patent Information

Application Number
CN202411643016.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-18
Publication Date
2025-11-04
Estimated Expiration
2044-11-18

AI Technical Summary

Technical Problem

Existing methods for acquiring 3D point cloud data of bridge real-world scenes mainly rely on surface sampling, which cannot simulate the real laser scanning process and cannot fully estimate the influence of factors such as scanning interval and occlusion, resulting in huge differences between the results and the real data.

Method used

A method for constructing a point cloud dataset using a bridge model and component parameters is proposed. The point cloud dataset is generated through triangulation and laser simulation scanning, including automatic correction of component parameters, uniform sampling, and addition of random noise to simulate the real laser scanning process.

Benefits of technology

The generated point cloud dataset can more accurately simulate bridge structures, making it suitable for classification and segmentation tasks in deep learning. It improves the realism and diversity of the dataset, meeting the needs of deep learning.

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Abstract

The application belongs to the field of point cloud data, and discloses a bridge real scene three-dimensional point cloud dataset production method, device and medium, the method comprises the following steps: obtaining dataset production demand; when the dataset production demand is the point cloud dataset production demand of the bridge component, obtaining a plurality of bridge components in the bridge model; extracting the component parameters of each bridge component, and constructing the point cloud dataset of the bridge component based on the component parameters of each bridge component and the parameters of the bridge model; and / or when the dataset production demand is the point cloud dataset production demand of the bridge model, obtaining the entity object corresponding to each bridge component in the bridge model; performing triangulation and laser simulation scanning on the surface of the entity object to obtain the point cloud space information of the entity object, and constructing the point cloud dataset of the bridge model according to the point cloud space information of the entity object. The dataset production method can efficiently generate the dataset meeting the deep learning demand.
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