Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching of 3-D curves using semi-differential invariants
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Free Form Shape Matching Using Deterministic Annealing and Softassign
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A scatter search-based technique for pair-wise 3D range image registration in forensic anthropology
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Hierarchical segmentation for unstructured and unfiltered range images
CGIV '07 Proceedings of the Computer Graphics, Imaging and Visualisation
Constraints for closest point finding
Pattern Recognition Letters
Replicator Dynamics in the Iterative Process for Accurate Range Image Matching
International Journal of Computer Vision
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An accurate, robust, and automatic registration of overlapping range images is usually a pre-requisite step for range image analysis and applications. While accurate depiction of object geometry requires the increase of the resolutions of images and thus, the amount of data to process, an efficient processing of such data then usually becomes an issue. In this paper, we first employ the efficient tensor analysis and k means clustering methods to hierarchically segment and cluster the original range images into a small number of planar patches represented as the closest points in the original images to their centroids. Then an advanced ICP variant is adopted to register such closest points. Finally, another ICP variant is used to refine the registration results obtained over all the points in the images. The experimental results based on real range images show that the proposed technique significantly outperforms the selected two state of the art ones for accurate and efficient registration of overlapping range images.