A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Hierarchical face clustering on polygonal surfaces
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Partitioning 3D Surface Meshes Using Watershed Segmentation
IEEE Transactions on Visualization and Computer Graphics
Pair-wise range image registration: a study in outlier classification
Computer Vision and Image Understanding - Registration and fusion of range images
Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Variational shape approximation
ACM SIGGRAPH 2004 Papers
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In orthodontics, occlusion is defined as the relationship between the upper and lower sets of teeth when the jaws are brought together. Understanding the nature of occlusion has important significance for the diagnosis and treatment of occlusal dysfunction and for planning reconstructive dentistry. The materials of study are 31 pairs of manually aligned dental study models. The upper and lower models are independently digitized using a laser surface scanner. Occlusion can be recovered by detecting and aligning a set of planes on the models. We describe a two-step procedure for determining the occlusal relationship using digitized dental models. The first step is a coarse alignment using four planar structures that are detected by K-means clustering, followed by principal component analysis. The second step is a refinement process using a variant of the iterative closest point technique. The quantitative results show that the algorithm is accurate, with an average measurement discrepancy of 0.74 mm between the physical and virtual models.