Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2004 Papers
A Formulation of Boundary Mesh Segmentation
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Segmentation of 3D Meshes through Spectral Clustering
PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
Feature Sensitive Mesh Segmentation with Mean Shift
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Point-Based Graphics
Skeleton-based Hierarchical Shape Segmentation
SMI '07 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007
Multi-resolution Hierarchical Point Cloud Segmenting
IMSCCS '07 Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences
Automatic segmentation of unorganized noisy point clouds based on the Gaussian map
Computer-Aided Design
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Point Cloud Segmentation Based on Radial Reflection
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
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Decomposition and segmentation of the objects represented by point cloud data become increasingly important for purposes like shape analysis and object recognition. In this paper, we propose a perception based approach to segment point cloud into distinct parts, and the decomposition is made possible of spatially close but geodetically distant parts. Curvature is a critical factor for shape representation, reflecting the convex and concave characteristics of an object, which is obtained by cubic surface fitting in our approach. To determine the number of patches, we calculate and select the critical feature points based on perception information to represent each patch. Taking the critical marker sets as a guide, each marker is spread to a meaningful region by curvature-based decomposition, and also further constraints are provided by the variation of normals. Then a skeleton extraction method is proposed and a label-driven skeleton simplification process is implemented. Further, a semantic graph is constructed according to the decomposed model and the skeletal structure. We illustrate the framework and demonstrate our approach on point cloud data to evaluate its function to decompose object shape based on human perceptions. Meanwhile, the result of decomposition is demonstrated with extracted skeletons. Performance of this algorithm is exhibited with experimental results, which proves its robustness to noise.