Intelligent building texture repairing and beautifying method fusing deep learning model

Through the intelligent repair method of architectural textures integrating deep learning models, the occlusion, drawing and distortion problems in building textures are identified and repaired, and the problem of manual repair is solved, automatic repair and beautification is realized, and the efficiency of three-dimensional model reconstruction is improved.

CN120471807AActive Publication Date: 2025-08-12WUDA GEOINFORMATICS CO LTD
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Patent Information

Application Number
CN202510961487.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-14
Publication Date
2025-08-12
Estimated Expiration
2045-07-14

AI Technical Summary

Technical Problem

In the prior art, there are problems such as occlusion, drawing, noise, and distortion after automatic mapping of building textures, which leads to a long time spent on manual repair and beautification, affecting the efficiency of urban three-dimensional model reconstruction.

Method used

The image low-rank matrix segmentation algorithm and SAM image segmentation algorithm are used to identify the area to be repaired, combined with LAMA and PatchMatch image repair algorithms for automatic repair, and used YOLO model to identify and crop billboards and text areas, and combined with the image diffusion model and ControlNet control diagram for texture beautification.

Benefits of technology

It realizes automated and intelligent restoration and beautification of building textures, reduces the time and cost of manual restoration, and improves the authenticity and efficiency of building models.

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Abstract

The invention is suitable for the technical field of building model data processing, and provides an intelligent building texture repairing and beautifying method fusing a deep learning model, and the method comprises the steps: firstly, carrying out the recombination of the texture of a three-dimensional building model, and generating a to-be-processed wall surface and roof texture picture; a to-be-repaired area, needing to be repaired, of the building texture is extracted by combining low-rank matrix decomposition and an SAM image segmentation algorithm, and then whether an LAMA image repair model or a PatchMatch image repair algorithm is used for texture processing is determined according to the proportion of the to-be-repaired area in a whole texture image so as to achieve the maximum repair effect. And finally, carrying out texture distortion repair and material beautification by using an image diffusion model, a ControlNet control chart and cue words, and outputting beautified wall surface and roof textures. The problems of texture shielding, garland, noise, window distortion and the like can be solved at the same time, automatic detection and automatic texture repairing and beautifying of the texture shielding garland area are achieved, and the time consumption of texture editing processing in the building monotonized modeling process is remarkably shortened.
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