利用红外偏振信息恢复透明物体形状，精度更高。
Shape from Polarization of Thermal Emission and Reflection
- 建立发射与反射联合偏振模型，提升建模准确性。
- 在真实数据集上实现厘米级形状估计误差，适用于多种材料。
- 适合做红外视觉、透明物体三维重建的研究者参考。
由于透明物体复杂的光传输特性，其形状估计极具挑战。为克服这一难题，本文在长波红外（LWIR）波段采用偏振形状恢复（SfP）技术，因多数材料在该波段呈不透明且可发射。尽管已有少量研究探索过LWIR SfP，但因偏振建模不足，尤其忽略反射影响，导致显著误差。为此，本文提出一个显式包含发射与反射共同作用的偏振模型。基于该模型，我们采用基于物理的直接方法与在物理合成数据上训练的神经网络学习方法估计表面法向。同时，构建了考虑系统误差的LWIR偏振成像模型以保证偏振测量精度。我们搭建原型系统并创建了首个真实世界基准数据集ThermoPol。大量实验表明，本方法在多种材料上均具有高精度与广泛适用性，包括可见光下透明的材料。
原文摘要 · Abstract (English)
Shape estimation for transparent objects is challenging due to their complex light transport. To circumvent these difficulties, we leverage the Shape from Polarization (SfP) technique in the Long-Wave Infrared (LWIR) spectrum, where most materials are opaque and emissive. While a few prior studies have explored LWIR SfP, these attempts suffered from significant errors due to inadequate polarimetric modeling, particularly the neglect of reflection. Addressing this gap, we formulated a polarization model that explicitly accounts for the combined effects of emission and reflection. Based on this model, we estimated surface normals using not only a direct model-based method but also a learning-based approach employing a neural network trained on a physically-grounded synthetic dataset. Furthermore, we modeled the LWIR polarimetric imaging process, accounting for inherent systematic errors to ensure accurate polarimetry. We implemented a prototype system and created ThermoPol, the first real-world benchmark dataset for LWIR SfP. Through comprehensive experiments, we demonstrated the high accuracy and broad applicability of our method across various materials, including those transparent in the visible spectrum.
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