DiffTex: Differentiable Texturing for Architectural Proxy Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914066618908672 |
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| author | Xiong, Weidan Wu, Yongli Zeng, Bochuan Guo, Jianwei Lischinski, Dani Cohen-Or, Daniel Huang, Hui |
| author_facet | Xiong, Weidan Wu, Yongli Zeng, Bochuan Guo, Jianwei Lischinski, Dani Cohen-Or, Daniel Huang, Hui |
| contents | Simplified proxy models are commonly used to represent architectural structures, reducing storage requirements and enabling real-time rendering. However, the geometric simplifications inherent in proxies result in a loss of fine color and geometric details, making it essential for textures to compensate for the loss. Preserving the rich texture information from the original dense architectural reconstructions remains a daunting task, particularly when working with unordered RGB photographs. We propose an automated method for generating realistic texture maps for architectural proxy models at the texel level from an unordered collection of registered photographs. Our approach establishes correspondences between texels on a UV map and pixels in the input images, with each texel's color computed as a weighted blend of associated pixel values. Using differentiable rendering, we optimize blending parameters to ensure photometric and perspective consistency, while maintaining seamless texture coherence. Experimental results demonstrate the effectiveness and robustness of our method across diverse architectural models and varying photographic conditions, enabling the creation of high-quality textures that preserve visual fidelity and structural detail. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffTex: Differentiable Texturing for Architectural Proxy Models Xiong, Weidan Wu, Yongli Zeng, Bochuan Guo, Jianwei Lischinski, Dani Cohen-Or, Daniel Huang, Hui Graphics Computer Vision and Pattern Recognition Simplified proxy models are commonly used to represent architectural structures, reducing storage requirements and enabling real-time rendering. However, the geometric simplifications inherent in proxies result in a loss of fine color and geometric details, making it essential for textures to compensate for the loss. Preserving the rich texture information from the original dense architectural reconstructions remains a daunting task, particularly when working with unordered RGB photographs. We propose an automated method for generating realistic texture maps for architectural proxy models at the texel level from an unordered collection of registered photographs. Our approach establishes correspondences between texels on a UV map and pixels in the input images, with each texel's color computed as a weighted blend of associated pixel values. Using differentiable rendering, we optimize blending parameters to ensure photometric and perspective consistency, while maintaining seamless texture coherence. Experimental results demonstrate the effectiveness and robustness of our method across diverse architectural models and varying photographic conditions, enabling the creation of high-quality textures that preserve visual fidelity and structural detail. |
| title | DiffTex: Differentiable Texturing for Architectural Proxy Models |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23336 |