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. The project page is at https://hyj542682306.github.io/SuperMat/.

翻译：从图像中分解基于物理的材质并还原其构成属性仍然具有挑战性，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，但由于需要多步去噪过程以及针对不同材质属性使用独立模型，它们面临着巨大的计算开销。我们提出了SuperMat，一种单步推理框架，能够通过一步推断实现高质量的材质分解。该框架支持结合感知损失与重渲染损失的端到端训练，并能在毫秒级速度下分解反照率、金属度和粗糙度贴图。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了跨视角一致的材质估计。实验表明，SuperMat在达到最先进的PBR材质分解质量的同时，将每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。项目页面位于 https://hyj542682306.github.io/SuperMat/。