Polarimetric Light Transport Analysis for Specular Inter-Reflection

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Computational Imaging Pub Date : 2024-03-23 DOI:10.1109/TCI.2024.3404612
Ryota Maeda;Shinsaku Hiura
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Abstract

Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of linear polarization. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. To decompose the reflectance components, we actively rotate the linear polarization of incident light and analyze the rotation direction of the reflected light. We evaluate our method using both synthetic and real data, demonstrating its effectiveness in decomposing specular inter-reflections of metal objects. Furthermore, we demonstrate that our method can be combined with other decomposition methods for a detailed analysis of light transport. As a practical application, we show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection.
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镜面相互反射的偏振光传输分析
众所周知,偏振能够分解漫反射和镜面反射。然而,现有的分解方法只关注直接反射,而忽略了多重反射,尤其是镜面相互反射。在本文中,我们提出了一种新的分解方法,利用一种独特的偏振特征来处理金属物体的镜面互反射:线性偏振的旋转方向。这种旋转方向是镜面反射和镜面间反射的区分因素。为了分解反射分量,我们主动旋转入射光的线偏振,并分析反射光的旋转方向。我们使用合成数据和真实数据对我们的方法进行了评估,证明了该方法在分解金属物体的镜面相互反射方面的有效性。此外,我们还证明了我们的方法可以与其他分解方法相结合,对光传输进行详细分析。在实际应用中,我们展示了该方法在提高针对强镜面相互反射的三维测量精度方面的有效性。
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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