Geometrically deformed models: a method for extracting closed geometric models form volume data
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Topology representing networks
Neural Networks
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Proceedings of the sixth ACM symposium on Solid modeling and applications
Self-Organizing Maps
Dynamic Catmull-Clark Subdivision Surfaces
IEEE Transactions on Visualization and Computer Graphics
An Adaptive Learning Approach for 3-D Surface Reconstruction From Point Clouds
IEEE Transactions on Neural Networks
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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This work introduces a method that produces triangular mesh representation of a target object surface. The new surface reconstruction method is based on Growing Self-organizing Maps, which learns both the geometry and the topology of the input data set. Each map grows incrementally producing meshes of different resolutions, according to different application needs. Experimental results show that the proposed method can produce triangular meshes having approximately equilateral faces, that approximate very well the shape of an object, including its concave regions and holes, if any.