Continuous skeleton computation by Voronoi diagram
CVGIP: Image Understanding
Computing and simplifying 2D and 3D continuous skeletons
Computer Vision and Image Understanding
Skeleton-based modeling operations on solids
SMA '97 Proceedings of the fourth ACM symposium on Solid modeling and applications
Accurate computation of the medial axis of a polyhedron
Proceedings of the fifth ACM symposium on Solid modeling and applications
Detecting undersampling in surface reconstruction
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
An Algorithm for the Medial Axis Transform of 3D Polyhedral Solids
IEEE Transactions on Visualization and Computer Graphics
A Formal Classification of 3D Medial Axis Points and Their Local Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
The power crust, unions of balls, and the medial axis transform
Computational Geometry: Theory and Applications
Simplified engineering analysis via medial mesh reduction
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Medial axis based solid representation
SM '04 Proceedings of the ninth ACM symposium on Solid modeling and applications
Distance functions and skeletal representations of rigid and non-rigid planar shapes
Computer-Aided Design
Discrete scale axis representations for 3D geometry
ACM SIGGRAPH 2010 papers
Interior Medial Axis Transform computation of 3D objects bound by free-form surfaces
Computer-Aided Design
A family of skeletons for motion planning and geometric reasoning applications
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - Representing and Reasoning About Three-Dimensional Space
Research on 3D medial axis transform via the saddle point programming method
Computer-Aided Design
A benchmark for surface reconstruction
ACM Transactions on Graphics (TOG)
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Several research have pointed out the potential use of the medial axis in various geometric modeling applications. The computation of the medial axis for a three dimensional shape often becomes the major bottleneck in these applications. Towards this end, in a recent work, we suggested an efficient algorithm that approximates the medial axis of a shape from a point sample. The input to this algorithm is only the coordinates of the sample points. As a result the approximation quality is limited by the input sample density. However, in geometric applications involving CAD models, the surfaces from which samples need to be derived are known. In this paper we present heuristics to take advantage of this a priori knowledge in our medial axis approximation algorithm. The quality of the approximation achieved by the method is surprisingly high as our experimental results exhibit.