用机器学习从穆勒矩阵中同时还原物体几何与材质属性。
Inferring geometry and material properties from Mueller matrices with machine learning
- 仅输入穆勒矩阵，训练模型预测材质和表面法向。
- 即使材质未知，仍能准确重建几何形状并识别材料类型。
- 对角线元素主导材质识别，非对角线元素决定法向估计。
穆勒矩阵（MMs）编码了物体几何与材质信息，但同时恢复二者是一个病态问题。本文探讨了在机器学习框架下，能否从穆勒矩阵中充分推断表面几何与材料属性。实验使用多种各向同性材料的球体数据集，覆盖全角度域，在五个可见光波长（450–650 nm）下采集穆勒矩阵。训练模型仅以这些穆勒矩阵为输入，预测材料属性与表面法向。结果表明，即便材料类型未知，模型仍可准确预测表面法向并重构物体几何；此外，穆勒矩阵使模型能够正确识别材料种类。进一步分析显示，对角元素对材质表征至关重要，而非对角元素则决定法向估计的准确性。
原文摘要 · Abstract (English)
Mueller matrices (MMs) encode information on geometry and material properties, but recovering both simultaneously is an ill-posed problem. We explore whether MMs contain sufficient information to infer surface geometry and material properties with machine learning. We use a dataset of spheres of various isotropic materials, with MMs captured over the full angular domain at five visible wavelengths (450-650 nm). We train machine learning models to predict material properties and surface normals using only these MMs as input. We demonstrate that, even when the material type is unknown, surface normals can be predicted and object geometry reconstructed. Moreover, MMs allow models to identify material types correctly. Further analyses show that diagonal elements are key for material characterization, and off-diagonal elements are decisive for normal estimation.
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