Key Techniques In Multiple Range Images Modeling

As a new realm, the content behind the Three-dimensional Imaging and Modeling (3DIM) involves multi-disciplines such as optics and optoelectronics, precision metrology, computer vision, computer graphics and so on. In hiberarchy, there are several key components regarding to the 3DIM, which include 3D optical sensing, 3D system calibration, range image registration and integration, 3D modeling and so forth. This dissertation focuses on the three key issues with respect to 3D modeling of multiple range images, namely, the range image registration, integration and the 3D model simplification.Range images registration is of a critical issue in 3D modeling. The registration of the range images taken from different view points aims to find the best estimate of rigid transformations among the pairwise range images and then put them into a common coordinate system. In terms of the registration the existed approaches can be classified into three distinct levels: the coarse registration, the fine registration of pairwise range images and the global registration of multiple range images. We propose a novel method for the coarse registration with the help of texture information by observing the fact that the 2D texture registration is not affected by geometry noise and can be performed rapidly. The experimental results have verified the efficiency and robustness of proposed technique.Furthermore, the most popular fine registration method is on the basis of the iterative closest point (ICP) algorithm, which was subjected to the four limitations: initial estimate for rigid transformation was needed, fragile to the noise or the outliers, error accumulation and with great time complexity. To overcome these disadvantages, in this thesis, we present a new fine registration method to register the multiple range-images with non-coding markers. In the proposed algorithm, a global optimization technique has been employed to reduce the error accumulation and the sensitivity to the noise and outliers. The experimental results illustrate that the proposed method is very efficient and time-saving compared with previous methods. In addition, we also make a thorough analysis on the difference between marker-based algorithm and ICP-based algorithm.Integration or data fusion of multiple range images is another paramount technique of 3D modeling. Up to now most of popular integration methods have been based on mesh stitching or implicit surface. All of these methods had their pros and cons either time-consumig or difficult to deal with real object surfaces with complex geometry and topology, and these methods were designed specifically for different applications. However, a fast, robust and high-accurate integration method need to be further developed. This thesis explores a new algorithm based on ray-casting, in which an axis-aligned bounding box tree is used to compute the intersections, and Dexel data structure is employed to save storage space. The experiment results show that a good range data fusion results can be obtained with the proposed method.Model simplification also plays a key role for 3D modeling, which would be viewed as an equivalent operation to the data compression in its counterpart of image processing. This thesis gives a comprehensive review to the most notably simplification algorithms already available in this field. Then we introduce a concept of sharp degree of vertex. With the concept of sharp degree of vertex, we can make an improvement to the quadric error metric (QEM) algorithm. In our approach, an penalty term is added to the measure function to make possible for adjusting the order of edge collapse. With proposed method, we are able to maintain the sharp feature of original model while achieving a large compression ratio in the simplified model. Experiment results show that the proposed method can give a better simplified model with dense meshes in the high-curvature regions and sparse meshes in the flat regions.