Research On Approaches To Automatic 3D Reconstruction Parametric Modeling Of Architectures Based On Image

With the development of information science, computer vision technology has been widely applied to many areas. The issue of 3-D information acquisition techniques based on computer vision has been one of the research hotspots in this field. The main idea of this thesis contains monocular vision techniques for estimating geometric properties of 3-D world from a series of 2-D digital images. Based on previous researches, significant progresses have been achieved after deeply study and analysis of former methods.Image segmentation is a necessary pretreatment step in many computer vision applications. In this thesis, a segmentation method especially for color images contain buildings is proposed. This approach makes use of area control pretreatment and the watershed algorithm to produce the original regions. The axial symmetry combined with other properties is estimated as region dissimilarity features. The final segmentation is derived by merging process. And the termination criterion determined through the distribution of the merging costs is an adaptive threshold. In addition, another segmentation algorithm based on scale-space image analysis is proposed. It analyzes hierarchical, geometric properties and the hue uniformity measure of the tree of scale-space in order to segment the objects. The results show that the approaches proposed can obtain effective regions of buildings in original images.The morphological sieve is a pervasive dynamic analysis frame since it can easily obtain the substantive characteristics of images. In this thesis, a new color morphological sieve with better synthetic performance proved by the experiments based on fuzzy theory is proposed. Besides, the morphological sieve is introduced into the monocular vision reconstruction process which results in a sufficient use of characteristics of the different scale sieves and good modeling results. After constructing the object-oriented parametric data model for the city building, the corners'properties are analyzed and the automatic building model estimation is realized. In the whole process, an effective segment optimization method is combined to improve the accuracy of the line and corner extraction.Automatic modeling of 3D object from images is a challenging problem in areas of computer vision especially from a single image. In this paper, a"City Building World"Bayesian model is constructed and a new automatically parameterized reconstruction method is presented which can recover the 3D cuboid architecture structure from a single terrestrial image. With the assumption that the interior parameters of the camera are known, the projection of the vanishing points is determined by exploiting the MAP. And from a few boundary pixels, the main parallelepiped contour is estimated. The cuboid model is reconstructed from the relationship between the parallelepiped geometry and the camera parameters.A parametric modeling method for the non-flat roof building is also presented. By using only six interactive input points, the roof corners can be detected automatically as well as the model geometry parameters. The proposed approaches and algorithms in this thesis are applied to synthetic data and real images respectively. At last, the rendering and virtual roaming dynamic web page is designed.