Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
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
Mesh Segmentation - A Comparative Study
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
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
Integrating Boundary Information in Pairwise Segmentation
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Multi-resolution Hierarchical Point Cloud Segmenting
IMSCCS '07 Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences
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
3D Mesh decomposition using Reeb graphs
Image and Vision Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved range image segmentation by analyzing surface fit patterns
Computer Vision and Image Understanding
3D mesh segmentation using mean-shifted curvature
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
A comparative study of existing metrics for 3D-mesh segmentation evaluation
The Visual Computer: International Journal of Computer Graphics
Watermarked 3-D Mesh Quality Assessment
IEEE Transactions on Multimedia
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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, but rather, they are mostly designed to allow inexperienced users to acquire surfaces and whole objects easily and independently. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. In this paper we address the task of segmenting the acquired surfaces into perceptually relevant parts. Such a problem is well known to be ill-defined both for 2D images and 3D objects, as even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reason recent years have seen a great research effort into semi-supervised approaches, that can make use of small bits of information provided by the user to attain better accuracy. We propose a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by propagating part labels over a weighted graph representation of the surface directly derived from its triangulated mesh. 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 a standard database of scanned 3D objects show its effectiveness even with moderate user supervision.