MatMart: Material Reconstruction of 3D Objects via Diffusion

• URL: http://arxiv.org/abs/2511.18900v1
• Date: Mon, 24 Nov 2025 08:58:14 GMT
• Title: MatMart: Material Reconstruction of 3D Objects via Diffusion
• Authors: Xiuchao Wu, Pengfei Zhu, Jiangjing Lyu, Xinguo Liu, Jie Guo, Yanwen Guo, Weiwei Xu, Chengfei Lyu,
• Abstract summary: tt achieves superior performance in material reconstruction compared to existing methods.<n>tt achieves both material prediction and generation capabilities through end-to-end optimization of a single diffusion model.
• Score: 36.79338202811421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applying diffusion models to physically-based material estimation and generation has recently gained prominence. In this paper, we propose \ttt, a novel material reconstruction framework for 3D objects, offering the following advantages. First, \ttt\ adopts a two-stage reconstruction, starting with accurate material prediction from inputs and followed by prior-guided material generation for unobserved views, yielding high-fidelity results. Second, by utilizing progressive inference alongside the proposed view-material cross-attention (VMCA), \ttt\ enables reconstruction from an arbitrary number of input images, demonstrating strong scalability and flexibility. Finally, \ttt\ achieves both material prediction and generation capabilities through end-to-end optimization of a single diffusion model, without relying on additional pre-trained models, thereby exhibiting enhanced stability across various types of objects. Extensive experiments demonstrate that \ttt\ achieves superior performance in material reconstruction compared to existing methods.

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