Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Surface Parameterization Using Riemann Surface Structure
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Hierarchical mesh segmentation based on fitting primitives
The Visual Computer: International Journal of Computer Graphics
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views
International Journal of Computer Vision
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and tracking of multiple video objects
Pattern Recognition
Consistent mesh partitioning and skeletonisation using the shape diameter function
The Visual Computer: International Journal of Computer Graphics
Fast mesh segmentation using random walks
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Randomized cuts for 3D mesh analysis
ACM SIGGRAPH Asia 2008 papers
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D mesh segmentation using mean-shifted curvature
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
SMI 2013: New evaluation metrics for mesh segmentation
Computers and Graphics
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A wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals. By converse, they are mostly designed to allow inexperienced users to easily and independently acquire surfaces and whole objects. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. Specifically, in this paper we concentrate on the segmentation task applied to the acquired surfaces. Such a problem is well known to be ill-defined both for 2D images and 3D objects. In fact, even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reasons, we refrain from any attempt to offer an automatic solution. Instead we introduce a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by iteratively visiting a weighted graph representation of the surface starting from the supplied seeds. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on scanned 3D objects show its effectiveness and easiness of use.