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标题:A Microfacet-based Reflectance Model for Photometric Stereo with Highly Specular Surfaces
作者:Lixiong Chen, Yinqiang Zheng, Boxin Shi, Art Subpa-Asa, Imari Sato
来源:International Conference on Computer Vision (ICCV 2017)
编译:陈世浪
审核:颜青松
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摘要
精确、稳定和可逆的表面反射率模型是实现立体视觉与现实世界匹配的关键。该领域的最新进展使各种类型的表面的形状恢复技术成为可能,但在高镜面反射率的情况下直接估计表面法线仍然难以实现。
在本文中,我们推导了一个基于各向同性微腔的反射系数模型,在此基础上为高镜面曲面量身定制了一个物理可解释的近似。利用这个近似,我们确定了曲面恢复问题与椭圆拟合问题的等价性,椭圆拟合问题可以描述为多项式系统。
此外,我们设计了一个快速、非迭代和全局最优解。在仿真图像和真实图像上的实验结果都验证了我们的模型,并证明了我们的解决方案在目标应用领域能够稳定地提供优越的性能。
Abstract
A precise, stable and invertible model for surface reflectance is the key to the success of photometric stereo with real world materials. Recent developments in the field have enabled shape recovery techniques for surfaces of various types, but an effective solution to directly estimating the surface normal in the presence of highly specular reflectance remains elusive.
In this paper, we derive an ana- lytical isotropic microfacet-based reflectance model, based on which a physically interpretable approximate is tailored for highly specular surfaces. With this approximate, we identify the equivalence between the surface recovery problem and the ellipsoid ofrevolution fitting problem, where the latter can be described as a system of polynomials.
Additionally, we devise a fast, non-iterative and globally optimal solver for this problem. Experimental results on both synthetic and real images validate our model and demonstrate that our solution can stably deliver superior performance in its targeted application domain.
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