Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces
Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces
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DOI：
10.1109/tpami.2014.2299798
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发表时间：
2014-09
影响因子：
23.6
通讯作者：
Satoshi Ikehata;D. Wipf;Y. Matsushita;K. Aizawa
中科院分区：
文献类型：
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作者：
Satoshi Ikehata;D. Wipf;Y. Matsushita;K. Aizawa
大多数用于非朗伯光度立体的传统算法可以分为两类。第一类建立在稳定的孤立点抑制技术之上，同时假定内置点具有密集的朗伯结构，因此当存在一般的漫射区域时，性能下降。第二种方法使用复杂的反射率表示和像素上的非线性优化来处理非朗伯曲面，但没有显式地考虑阴影或其他形式的损坏异常值。在这篇文章中，我们提出了一种纯像素的光度立体方法，通过假设外观可以分解成稀疏的非漫射分量(例如阴影、镜面等)，稳定而有效地处理各种非Lambertian效果。以及由表面法线和照明点积的单调函数表示的漫射分量。该函数是使用对逆漫反射模型的分段线性近似来构造的，从而在不存在非漫反射污染的情况下得到曲面法线和模型参数的闭合形式估计。后者被建模为嵌入在分层贝叶斯模型中的潜在变量，以便我们可以准确地计算未知表面法线，同时分离漫反射和非漫反射分量。进行了广泛的评估，显示了使用合成图像和真实世界图像的最先进的性能。
Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into two categories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertian structure for the inliers, and thus performance degrades when general diffuse regions are present. The second utilizes complex reflectance representations and non-linear optimization over pixels to handle non-Lambertian surfaces, but does not explicitly account for shadows or other forms of corrupting outliers. In this paper, we present a purely pixel-wise photometric stereo method that stably and efficiently handles various non-Lambertian effects by assuming that appearances can be decomposed into a sparse, non-diffuse component (e.g., shadows, specularities, etc.) and a diffuse component represented by a monotonic function of the surface normal and lighting dot-product. This function is constructed using a piecewise linear approximation to the inverse diffuse model, leading to closed-form estimates of the surface normals and model parameters in the absence of non-diffuse corruptions. The latter are modeled as latent variables embedded within a hierarchical Bayesian model such that we may accurately compute the unknown surface normals while simultaneously separating diffuse from non-diffuse components. Extensive evaluations are performed that show state-of-the-art performance using both synthetic and real-world images.