Variational Perspective Shape from Shading-猫眼课题宝

DOI：

10.1007/978-3-319-18461-6_43

发表时间：

2015-05

期刊：

影响因子：

通讯作者：

中科院分区：

其他

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作者：

Y. Ju;Andrés Bruhn;M. Breuß

最近的许多方法的透视形状从阴影（SfS）是基于配方的偏微分方程（PDE）。然而，虽然这些方法的质量稳步提高，但它们缺乏鲁棒性仍然是一个悬而未决的问题。在这种情况下，变分方法似乎是一个很有前途的替代方案，因为它们允许将平滑假设，已被证明是有用的许多其他任务在计算机视觉。然而，令人惊讶的是，到目前为止，这样的方法几乎没有被考虑用于透视SfS。在我们的文章中，我们解决这个问题，并制定了一个新的变分模型，这项任务。通过结合最近的PDE为基础的方法，如朗伯反射模型和相机为中心的照明与不连续性保持二阶平滑项的积木，我们得到了一个变分方法的角度SFS，通过建设提供了一个改进的鲁棒性程度相比，现有的PDE为基础的方法。我们的实验证实了我们的策略是成功的。他们表明，将基于偏微分方程的方法的假设嵌入到具有适当平滑项的变分模型中可能非常有益-特别是在具有噪声或部分缺失信息的情况下。

Many recent methods for perspective shape from shading (SfS) are based on formulations in terms of partial differential equations (PDEs). However, while the quality of such methods steadily improves, their lacking robustness is still an open issue. In this context, variational methods seem to be a promising alternative, since they allow to incorporate smoothness assumptions that have proven to be useful for many other tasks in computer vision. Surprisingly, however, such methods have hardly been considered for perspective SfS so far. In our article we address this problem and develop a novel variational model for this task. By combining building blocks of recent PDE-based methods such as a Lambertian reflectance model and camera-centred illumination with a discontinuity-preserving second-order smoothness term, we obtain a variational method for perspective SfS that offers by construction an improved degree of robustness compared to existing PDE-based approaches. Our experiments confirm the success of our strategy. They show that embedding the assumptions of PDE-based approaches into a variational model with a suitable smoothness term can be very beneficial – in particular in scenarios with noise or partially missing information.

Variational Perspective Shape from Shading

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