With the improvements in range image acquisition by optical metrology of our group, we also developed a novel method
for the registration and integration of range images. The registration approach is based on texture-feature recognition.
Texture-feature pairs in two texture images are identified by cross-correlation, and the validity-checking is implemented
through Hausdorff distance comparison. The correspondence between the texture image and range image helped acquire
the range point-pairs, and the initial transformation of two range images was computed by least-squares technique. With
this initial transformation, the fine registration was achieved by ICP algorithm. The integration of the registered range
images is based on ray casting. An axis-aligned bounding box for all range images is computed. Three bundles of
uniform-distributed rays are cast and pass through the faces of the box along three orthogonal coordinate axes
respectively. The intersections between the rays and the range images are computed and stored in Dexels. The KD-tree
structure is used to accelerate computation. Those data points in overlapped region are identified with specific criteria
based on the distance and the angle of normals. We can obtain a complete non-redundant digital model after removing
the overlapped points. The experimental results illustrate the efficiency of the method in reconstructing the whole three dimensional
During the process of the real object virtualization, the data obtained from the digital system, such as the three-dimension imaging and modeling system (3DIMS), are very large. Even after the processing based on reverse engineering, the resulting image still can't satisfy the demands of computer animation, etc. So a novel approach based on data reduction and subdivision is presented in this article. The experiment results are also given to show the effectiveness of proposed method.