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Unifying Approaches and Removing Unrealistic Assumptions in Shape from Shading: Mathematics Can Help

机译:从阴影中统一和消除形状的不切实际的假设:数学可以帮助

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This article proposes a solution of the Lambertian Shape From Shading (SFS) problem by designing a new mathematical framework based on the notion of viscosity solutions. The power of our approach is twofolds: 1) it defines a notion of weak solutions (in the viscosity sense) which does not necessarily require boundary data. Note that, in the previous SFS work of Rouy et al. [23,15], Falcone et al. [8], Prados et al. [22,20], the characterization of a viscosity solution and its computation require the knowledge of its values on the boundary of the image. This was quite unrealistic because in practice such values are not known. 2) it unifies the work of Rouy et al. [23,15], Falcone et al. [8], Prados et al. [22,20], based on the notion of viscosity solutions and the work of Dupuis and Oliensis [6] dealing with classical (C~1) solutions. Also, we generalize their work to the perspective SFS problem recently introduced by Prados and Faugeras 20. Moreover this article introduces a generic formulation of the SFS problem. This generic formulation summarizes various (classical) formulations of the Lambertian SFS problem. In particular it unifies the orthographic and the perspective SFS problems. This generic formulation significantly simplifies the formalism of the problem. Thanks to this generic formulation, a single algorithm can be used to compute numerical solutions of all these previous SFS formulations. Finally we propose two algorithms which provide numerical approximations of the new weak solutions of the generic SFS problem. These provably convergent algorithms are quite robust and do not necessarily require boundary data.
机译:本文通过基于粘度溶液的概念设计新的数学框架,提出了兰伯语形状的遮蔽(SFS)问题。我们方法的力量是双重的:1)它定义了不一定需要边界数据的弱解决方案的概念。请注意,在先前的Rouy等人的SFS工作中。 [23,15],Falcone等人。 [8],Prados等。 [22,20],粘度溶液的表征及其计算要求在图像的边界上了解其值。这是非常不切实际的,因为在实践中,这些价值是未知的。 2)它统一了Rouy等人的工作。 [23,15],Falcone等人。 [8],Prados等。 [22,20],基于粘度溶液的概念和Dupuis和Oliensis的工作[6]处理经典(C〜1)溶液。此外,我们将他们的工作概括为Prados和Faugeras 20最近引入的透视SFS问题。此外,本文介绍了SFS问题的通用配方。这种通用制剂总结了Lambertian SFS问题的各种(经典)制剂。特别是它统一了正交和透视的SFS问题。这种通用配方显着简化了问题的形式主义。由于这种通用配方,单个算法可用于计算所有这些先前的SFS制剂的数值解决方案。最后,我们提出了两种算法,该算法提供了通用SFS问题的新弱解的数值近似。这些可怕的收敛算法非常强大,不一定需要边界数据。

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