DANTE-W: Diffuse Albedo Neural Texturing in the Wild

• URL: http://arxiv.org/abs/2606.30677v1
• Date: Fri, 26 Jun 2026 09:45:56 GMT
• Title: DANTE-W: Diffuse Albedo Neural Texturing in the Wild
• Abstract summary: We present a framework for recovering texture from unstructured image collections.<n>Our method integrates texture reconstruction with traditional 3D pipelines.<n>We evaluate our approach in reconstructing accurate textures and boosting fidelity.
• Score: 15.545994699853813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classical mesh texturing techniques blend captured multi-view images directly, which inevitably suffer from baked-in shading and casted shadows that compromise visual fidelity during relighting. To circumvent this issue, we present a neural texturing framework, namely DANTE-W, to enable high-fidelity diffuse albedo texture recovery from unstructured image collections for large-scale, in-the-wild scenes, which integrates seamlessly with traditional 3D reconstruction pipelines. Given a reconstructed mesh and its surface parameterization, our method fuses view-space generative albedo priors into a coherent texture space via an expressive neural representation, while substantially enhancing fine-grained textural details through physically principled neural rendering. To comprehensively evaluate our method, we curate a benchmark dataset featuring diverse, fine-grained textures, comprising both real-world in-the-wild scenes and synthetic objects. Extensive experiments verify the effectiveness of our approach in reconstructing accurate albedo textures and boosting relighting fidelity. Project page: dante-wild.github.io.

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