Object recognition based on moment (or algebraic) invariants
Geometric invariance in computer vision
On the definition and the construction of pockets in macromolecules
Discrete Applied Mathematics - Special volume on computational molecular biology DAM-CMB series volume 2
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the conference on Visualization '01
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
The flow complex: a data structure for geometric modeling
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Shock Scaffold for Representing 3D Shape
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Provable surface reconstruction from noisy samples
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
The power crust, unions of balls, and the medial axis transform
Computational Geometry: Theory and Applications
Cover geometry design using multiple convex hulls
Computer-Aided Design
Isometry-invariant matching of point set surfaces
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
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We present the implementation results of a shape segmentation technique and an associated shape matching method whose input is a point sample from the shape. The sample is allowed to be noisy in the sense that they may scatter around the boundary of the shape instead of lying exactly on it. The algorithm is simple and mostly combinatorial in that it builds a single data structure, the Delaunay triangulation of the point set, and groups the tetrahedra to form the segments. A small set of weighted points are derived from the segments which are used as signatures to match shapes. Experimental results establish the effectiveness of the method in practice.