Enhancing 3D Building Model Textures with Super-Resolution of Aerial Photographs
We have applied a super-resolution technique to enhance the texture image quality of LOD2 building models. Specifically, we adopted SwinIR for upscaling low-resolution images. In order to achieve better results, several approaches for creating training data, consisting of pairs of low-resolution and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/349/2025/isprs-annals-X-G-2025-349-2025.pdf |
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| Summary: | We have applied a super-resolution technique to enhance the texture image quality of LOD2 building models. Specifically, we adopted SwinIR for upscaling low-resolution images. In order to achieve better results, several approaches for creating training data, consisting of pairs of low-resolution and high-resolution image were investigated. The results showed that training with low-resolution images created by downsampling high-resolution images by a factor of four and then applying blurring and noise improved the sharpness of building edge lines in super-resolution images. Training data with augmentation techniques, such as the use of random noise and random rotation, are proved to be effective in enhancing super-resolution images. Using the super-resolved images, LOD2 building models were created, and a subjective evaluation of the building roof texture quality was conducted. The results indicated that for the input images used in super-resolution, 87% of buildings from high-quality aerial photographs and 78% from lower-quality photographs were rated as having sharp edges without distortion. Even with limited training data, the developed method was able to achieve high-quality super-resolution, regardless of the input image quality, leading to improved texture quality in LOD2 building models. |
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| ISSN: | 2194-9042 2194-9050 |