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Shading through Defocus

机译:通过散焦着色

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摘要

Traditional shape from defocus has been based on modeling the defocusing process through a normalized point spread function (PSF). Here we show that, in the general case, the normalization factor will depend on the depth map, what precludes shape estimation. If the camera is focused at far distances, however, such dependence can be neglected and an unnormalized PSF can be employed. We thus reformulate Pentland's shape from defocus approach using unnormalized gaussians, and prove that, under certain assumptions, such model allows the estimation of a dense depth map from a single input image. Moreover, by using unnormalized Gabor functions as a generalization of the unnormalized-gaussian PSF, we are able to approximate any signal as resulting from a series of local, frequency-dependent defocusing processes, to which the modified Pentland's approach also applies. Such approximation proves suitable for shading images, and has allowed us to obtain good shape-from-shading estimates essentially through a shape-from-defocus approach, without resorting to the reflectance map concept.
机译:散焦的传统形状基于通过归一化点扩散函数(PSF)对散焦过程进行建模的基础。在这里,我们表明,在一般情况下,归一化因子将取决于深度图,而深度图排除了形状估计的可能性。但是,如果将相机聚焦在远处,则可以忽略这种依赖性,并且可以使用未标准化的PSF。因此,我们使用非归一化的高斯从散焦方法重新构造了Pentland的形状,并证明了在某些假设下,这种模型允许从单个输入图像估计密集的深度图。此外,通过使用未归一化的Gabor函数作为未归一化的高斯PSF的推广,我们能够近似由一系列局部的,依赖于频率的散焦过程产生的任何信号,改进的Pentland方法也适用于此。这种近似证明适用于阴影图像,并已使我们基本上可以通过从散焦形状的方法获得良好的从阴影形状的估计,而无需求助于反射图的概念。

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