Decomposing physically-based materials from images into their constituent properties remains challenging, particularly when maintaining both computational efficiency and physical consistency. While recent diffusion-based approaches have shown promise, they face substantial computational overhead due to multiple denoising steps and separate models for different material properties. We present SuperMat, a single-step framework that achieves high-quality material decomposition with one-step inference. This enables end-to-end training with perceptual and re-render losses while decomposing albedo, metallic, and roughness maps at millisecond-scale speeds. We further extend our framework to 3D objects through a UV refinement network, enabling consistent material estimation across viewpoints while maintaining efficiency. Experiments demonstrate that SuperMat achieves state-of-the-art PBR material decomposition quality while reducing inference time from seconds to milliseconds per image, and completes PBR material estimation for 3D objects in approximately 3 seconds.

翻译：从图像中分解基于物理的材质并还原其构成属性仍具挑战性，尤其是在同时保持计算效率与物理一致性的情况下。尽管近期基于扩散模型的方法展现出潜力，但由于需要多次去噪步骤及针对不同材质属性的独立模型，它们面临着巨大的计算开销。本文提出SuperMat——一种通过单步推理即可实现高质量材质分解的一步式框架。该框架支持端到端训练，结合感知损失与重渲染损失，同时能在毫秒级速度下分解反照率、金属度与粗糙度贴图。我们进一步通过UV优化网络将框架扩展至三维物体，在保持效率的同时实现跨视角一致的材质估计。实验表明，SuperMat在达到当前最优PBR材质分解质量的同时，将单幅图像的推理时间从秒级缩短至毫秒级，并能在约3秒内完成三维物体的PBR材质估计。