3D mesh segmentation using mean-shifted curvature
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
Variational mesh decomposition
ACM Transactions on Graphics (TOG)
Markov random fields for improving 3D mesh analysis and segmentation
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Geodesic Polar Coordinates on Polygonal Meshes
Computer Graphics Forum
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In this paper, we introduce a versatile and robust method for analyzing the feature space associated with a given mesh surface. The method is based on the mean-shift operator, which was shown to be successful in image and video processing. Its strength lies in the fact that it works in a single joint space of geometry and attributes called the feature-space. The mean-shift procedure works as a gradient ascend finding maxima of an estimated probability density function in feature-space. Our method for using the mean-shift technique on surfaces solves several difficulties. First, meshes as opposed to images do not present a regular and uniform sampling of domain. Second, on surface meshes the shifting procedure must be constrained to stay on the surface and preserve geodesic distances. We define a special local geodesic parameterization scheme, and use it to generalize the mean-shift procedure to unstructured surface meshes. Our method can support piecewise linear attribute definitions as well as piecewise constant attributes.