Key Techniques Of Data Processing In 3D Shape Measurement System

Digital model construction by measuring the shape of real-world objects has been widely used in an extensive range of fields, such as aerospace, automotive, shipbuilding, machinery manufacturing, biomedical, gaming and entertainment, etc. As a bridge built between the data acquisition and the data application, data processing is an important part of the 3D shape measurement. With the rapid development of digital imaging and image processing technology, 3D measurement systems with area-array cameras as the main sensors have made great progress in recent years. Several key techniques of data processing in this type of systems are studied in this paper, including data preprocessing, crude registration and fine registration of multi-view range images, range images integration, mesh smoothing and simplification. The main contents and innovations are summarized as follows.By utilizing both the depth information and the pixel structure information, a dense point clouds triangulation method and an adaptive sampling triangulation method are presented respectively. With the dense point clouds triangulation method, a full resolution mesh model of the range image can be quickly constructed. Whereas the adaptive sampling triangulation method, which is designed for real-time display of the massive measurement data, generates a mesh model with high appearance fidelity from significantly reduced points.Two crude registration methods are put forward based on two different principles. In the first one, the feature points are extracted directly from the 3D meshes by a new corner detection algorithm proposed. Having established the correspondences between two range images, the crude registration is achieved by the least square method. In the second one, the range image is mapped into a 2D artificial intensity image according to the shape index of every point in the range image. By using feature detection and matching methods for intensity image, feature points in the artificial image are extracted and matched, and then the 3D point matches are obtained indirectly by the mapping. After further steps for removing mismatches, the crude registration is carried out finally. Experiments show that both proposed methods are robust against the overlapping extent and the noise. The second method shows higher stability to register the range images with less geometric features.The theories and methods of fine registration of multi-view measurement data are deeply studied. In particular, an algorithm is proposed to improve the global fine registration which is based on the virtual spring force. There are two major improvements. First, to overcome the assumption in the original algorithm that the external points do not exist, the boundary constraint is imposed on the process searching for the nearest corresponding points. The corresponding weights are adaptively set according to the registration error in each iteration to improve the registration accuracy. Besides, the parallel acceleration technology based on GPU is adopted to improve the efficiency of the correspondance searching process.An algorithm for simultaneously merging the registered range images is proposed. Each range image is in turn defined as the benchmark image, and on this basis, the overlapping areas between the benchmark with the other range images are detected simultaneously and adjusted optimally to reduce the accumulated error. To connect the gaps smoothly between the images after redundancy removal, an overlapping constraint as well as a weighing scheme for the boundary points is presented. The overlapping and removal information are took into account in the stitching process, which simplifies the stitching algorithm, and a complete topological manifold mesh surface can be generated without re-triangulation or additional new points. Together with the geometric data integration, a texture blending method is presented to generate smooth texture fusion under the same framework. Experiments demonstrate the validity and efficiency of the proposed algorithm.A hybrid filtering algorithm with anisotropic is proposed. The bilateral filtering operator based on two-order neighborhood faces is defined and compared with another operator based on one-order neighborhood points in detail, The different performance of the two operators in smoothing noise and preserving features is considered, and a new hybrid filtering operator is proposed by combining the two operators with adaptive weights. The new operator is effective in filtering noise while keeping the smooth areas little changed. It is also superior in suppressing the mesh contraction or expansion that could be available after lots of iterations.An improvement to the quadric-error-metrics(QEM)-based mesh simplification algorithm is presented. The support region on the original mesh is defined and searched for the every collapse edge in the simplifying process. The connection between the collapse edge and the original mesh is accordingly established. The quadric error from the new vertex of the collapse edge to its support region is calculated as the global simplification error and is introduced into the total cost function of QEM. Experimental results demonstrate that the improved algorithm reduces the simplification error obviously and preserves the original mesh details better.