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Current point cloud processing algorithms do not have the capability toautomatically extract semantic information from the observed scenes, except invery specialized cases. Furthermore, existing mesh analysis paradigms cannot bedirectly employed to automatically perform typical shape analysis tasksdirectly on point cloud models.  We present a potent framework for shape analysis, similarity, andsegmentation of noisy point cloud models for real objects of engineeringinterest, models that may be incomplete. The proposed framework relies onspectral methods and the heat diffusion kernel to construct compact shapesignatures, and we show that the framework supports a variety of clusteringtechniques that have traditionally been applied only on mesh models. Wedeveloped and implemented one practical and convergent estimate of theLaplace-Beltrami operator for point clouds as well as a number of clusteringtechniques adapted to work directly on point clouds to produce geometricfeatures of engineering interest. The key advantage of this framework is thatit supports practical shape analysis capabilities that operate directly onpoint cloud models of objects without requiring surface reconstruction orglobal meshing. We show that the proposed technique is robust against typicalnoise present in possibly incomplete point clouds, and segment point cloudsscanned by depth cameras (e.g. Kinect) into semantically-meaningful sub-shapes.

Practical Shape Analysis and Segmentation Methods for Point Cloud Models