通过估算微粗糙表面的特性来提高形状恢复

H. Ragheb, E. Hancock
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引用次数: 1

摘要

我们说明了在计算机视觉表面分析问题中使用贝克曼公式的基尔霍夫理论。贝克曼模型是描述粗糙表面的光反射率的物理模型。在这里,我们使用贝克曼模型的修改形式,使用C.L. Vernold和J.E. Harvey的修改(见Proc. SPIE, vol.3426, p.51-6, 1998)。模型的参数为表面粗糙度和相关长度。我们展示了如何使用镜面反射特性来估计表面粗糙度。我们还提出了一种利用不同照明方向的表面图像对估计相关长度的技术。有了这些参数,贝克曼模型可以用来进行光度校正,因此可以将阴影形状应用于校正后的兰伯特图像,以恢复改进的形状。这个模型也可以用来重新照亮恢复的表面。我们提供实验来说明该方法对这些任务的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving shape recovery by estimating properties of slightly-rough surfaces
We illustrate the use of the Beckmann formulation of the Kirchhoff theory for surface analysis problems in computer vision. The Beckmann model is a physical model that describes the reflectance of light from rough surfaces. Here, we use the modified form of the Beckmann model for slightly-rough surfaces using the modification of C.L. Vernold and J.E. Harvey (see Proc. SPIE, vol.3426, p.51-6, 1998). The parameters of the model are the surface roughness and the correlation length. We show how the surface roughness can be estimated using the specular reflectance properties. We also propose a technique for estimating the correlation length using pairs of surface images, subject to different illumination directions. With these parameters to hand, the Beckmann model may be used to perform photometric correction, and hence shape-from-shading may be applied to the corrected Lambertian image to recover improved shape. This model may also be used to re-illuminate the recovered surface. We present experiments to illustrate the utility of the method for each of these tasks.
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