Image 3D Object Reconstruction

• Main Authors: Wen-Chang Cheng, 鄭文昌
• Other Authors: Chin-Teng Lin
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/77908158453333597116

博士 === 國立交通大學 === 電機與控制工程系所 === 93 === In this thesis, we propose three new techniques to improve the surface reconstruction and color reconstruction of 3D objects. For the surface reconstruction of 3D objects, photometric stereo is able to estimate local surface orientations by using several images of the same surface which are photographed from the same viewpoint but under the illuminations from different directions. According to previous researches, a successful reflectance model for surface reconstruction of 3D objects should combine two major components, the diffusion and specular components. As a result, in this thesis, we categorize the improvement by our methodology into two stages. In the first stage, a new neural-network-based adaptive hybrid-reflectance model is proposed for combining the diffusion and specular components automatically. The supervised learning algorithm is adopted and the hybrid ration for each point is updated in the learning iterations. After the learning process, the neural network can estimate the normal vector for each point on the surface of 3D objects in an image. The enforcing integrability method is applied to the reconstruction of 3D objects by using the obtained normal vectors. The experimental results demonstrate that the proposed network estimates the point-wisely adaptive combination ratio of the diffusion and specular intensities such that the different reflection properties of each point on the object surface are considered to achieve better performance on the surface reconstruction. In the second stage, we further propose a new nonlinear reflectance model consisting of diffusion and specular components for modeling the surface reflectance of 3D objects in an image. Unlike the previous approaches, these two components are not separated and processed individually in the proposed model. An unsupervised learning adaptation algorithm is developed to estimate the reflectance model based on image intensities. In this algorithm, the post-nonlinear independent component analysis (ICA) is used to obtain the surface normal on each point of an image. Then, the 3D surface model is reconstructed based on the estimated surface normal on each point of image by using the enforcing integrability method. The results clearly indicate the superiority of the proposed nonlinear reflectance model over the other linear hybrid reflectance model. The experimental results demonstrate that the post-nonlinear ICA method can be used in the problems of surface reconstruction. For color recovering of 3D objects, a new neural-network-based algorithm for surrounding illumination estimation of image scenes is proposed. This estimation is based upon the chromaticity histogram of a color image, which is obtained by the accumulation of CIE chromaticity values corresponding to all the colors in the image. A neural network with a BP learning algorithm is used to model the nonlinearly functional relationship between the central values of the chromaticity histogram and the coefficients of illuminant functions. The trained BP network can then be used to estimate the spectral power distribution of the surrounding illuminant. By substituting this illuminant estimates into the finite-dimensional linear model of surface reflectance, the colors of the image can be recovered with the standard illuminant (such as D65) for color constancy. The experimental results show that the new algorithm outperforms the existing popular compared algorithms, both in quantitative error indices and in qualitative visual perception.