Multi-modal data three-dimensional scene reconstruction method and device

By acquiring and processing multimodal data, and combining deep learning and feature fusion techniques, the problem of insufficient accuracy in complex scenes by traditional 3D scene reconstruction methods has been solved, achieving high-precision 3D scene reconstruction that is applicable to fields such as virtual reality and augmented reality.

CN120953484APending Publication Date: 2025-11-14JIANGSU BASIC GEOGRAPHIC INFORMATION CENT
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Patent Information

Application Number
CN202510972090.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-15
Publication Date
2025-11-14

AI Technical Summary

Technical Problem

Traditional single-modal 3D scene reconstruction methods are easily affected by lighting conditions and similar texture regions, resulting in difficulty in image feature matching and decreased reconstruction accuracy in scenes with low texture or drastic lighting changes.

Method used

A multimodal data-based 3D scene reconstruction method is adopted, which uses optical cameras, lidar and depth cameras to acquire color images, point cloud data and depth images. The data preprocessing and feature fusion are combined with median filtering, feature point matching, deep learning and attention mechanisms. A 3D scene model is constructed through coarse alignment and fine alignment, and then optimized and rendered.

Benefits of technology

It significantly improves the accuracy and reliability of 3D scene reconstruction, and can accurately reconstruct the geometric structure and texture information of objects in complex scenes, meeting the needs of virtual reality and augmented reality for high-precision, high-realism models.

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Abstract

The invention relates to the technical field of three-dimensional scene reconstruction methods, in particular to a multi-modal data three-dimensional scene reconstruction method and device, and the method specifically comprises the following steps: 1, carrying out the data collection of a target scene through a plurality of sensors, comprising a color image sequence acquired by an optical camera, point cloud data acquired by a laser radar and a depth image sequence acquired by a depth camera; 2, denoising processing is carried out on the collected color image sequence, and a median filtering algorithm is adopted; 3, rough alignment is carried out on preprocessed multi-modal data by adopting a method based on feature points; according to the method, the alignment precision is remarkably improved, accurate matching of different modal data in space is ensured, the geometric structure of a reconstruction model is accurate, and the relative position of an object fits an actual scene.
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