反射图建模的表面重建算法
3D Surface Reconstruction Based on Reflectance Map Model
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摘要: 提出一种利用单幅图像的灰度估计表面形状的方法.对于一幅图像,使用单隐层小波神经网络建立非线性反射图函数模型,通过神经网络的训练最小化误差函数得到网络权值.利用变分法得到景物最终表面高度值,并引入分级实现降低计算量. 以此模型为基础,不再需要预知光源参数.实验结果表明了该方法的有效性,且其在恢复精度上有所提高.Abstract: A framework for the recovery of smooth surface shapes from shading images with unknown light source is presented.A single hidden layer wavelet neural network is used to approximate the complex nonlinear reflectance map function and reconstruct the shape as minimizing an error function over the network weight. Object surface is recovered with recursive algorithm and hierarchical implementation is employed to reduce the computation.Under the proposed model,the lighting parameters are not necessary. Experimental results demonstrate the new algorithm is more robust and accurate than traditional ones.
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