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Recovering Three-Dimensional Surfaces with Multi-images Shape-From-Shading Method

机译:用多图像形状从阴影方法恢复三维表面

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Three-dimensional (3-D) shape reconstruction is one of the fundamental problems in the field of computer version. Most existing shape-from-shading (SFS) methods are based on signal image and orthogonal projection. But the reflectance map equation is a nonlinear partial differential equation about two random variables. So the SFS is an ill-posed problem. Further more, orthogonal projection used to simulate the imaging processes of camera is not very accurate. This paper proposes a new SFS method under perspective projection with multi-images. Three images with different lighting source directions are captured by camera firstly. Following three reflectance map equations which are described by Lambertain model are established. Then the gradient vectors of the 3-D surface are calculated by solving the reflectance map equations. The gray constraint and gradient component constraint conditions are used to construct target function, and the corresponding Eulor-Poision equations are derived. Simultaneously, discrete difference is used to approximate differential operation. New iterative 3-D shape reconstruction algorithm is proposed by the discrete difference equation. Three pixel values are used to solve certain gradient value in our method. So the ill-posed problem in traditional SFS which solves a single reflectance map equation can be avoided. At last, experimental results of 3-D reconstruction show that the proposed method is effective.
机译:三维(3-D)形状重建是计算机版本中的基本问题之一。大多数现有的形状从阴影(SFS)方法基于信号图像和正交投影。但反射率映射方程是关于两个随机变量的非线性偏微分方程。所以SFS是一个弊病的问题。此外,用于模拟相机的成像过程的正交投影不是很准确。本文提出了一种在具有多图像的透视投影下的新SFS方法。使用不同的照明源方向的三个图像首先被相机捕获。在建立由兰伯特模型描述的三个反射率映射方程之后。然后通过求解反射率映射方程来计算三维表面的梯度向量。灰色约束和渐变分量约束条件用于构造目标函数,并且导出相应的eulor-poision方程。同时,使用离散差异来近似差分操作。采用离散差分方程提出了新的迭代3-D形重建算法。三个像素值用于解决我们方法中的某些梯度值。因此,可以避免解决传统SFS中的不良问题,其解决单一反射率映射等式。最后,3-D重建的实验结果表明,该方法是有效的。

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