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
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Visualization and Computer Graphics
A Fast and Efficient Projection-Based Approach for Surface Reconstruction
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
Reconstruction of B-Spline Surfaces from Scattered Data Points
CGI '00 Proceedings of the International Conference on Computer Graphics
Approximation with Active B-Spline Curves and Surfaces
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Radial Basis Functions
A concept for parametric surface fitting which avoids the parametrization problem
Computer Aided Geometric Design
Surface reconstruction based on compactly supported radial basis functions
Geometric modeling
Ridge-valley lines on meshes via implicit surface fitting
ACM SIGGRAPH 2004 Papers
Implicit Fitting and Smoothing Using Radial Basis Functions with Partition of Unity
CAD-CG '05 Proceedings of the Ninth International Conference on Computer Aided Design and Computer Graphics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Introduction to Neural Networks with Java
Introduction to Neural Networks with Java
Automatic sequence of 3D point data for surface fitting using neural networks
Computers and Industrial Engineering
Provable surface reconstruction from noisy samples
Computational Geometry: Theory and Applications
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
3D freeform surfaces from planar sketches using neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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We present a new point set surfacing method based on a data-driven mapping between the parametric and geometric spaces. Our approach takes as input an unstructured and possibly noisy point set representing a two-manifold in R^3. To facilitate parameterization, the set is first embedded in R^2 using neighborhood-preserving locally linear embedding. A learning algorithm is then trained to learn a mapping between the embedded two-dimensional (2D) coordinates and the corresponding three-dimensional (3D) space coordinates. The trained learner is then used to generate a tessellation spanning the parametric space, thereby producing a surface in the geometric space. This approach enables the surfacing of noisy and non-uniformly distributed point sets. We discuss the advantages of the proposed method in relation to existing methods, and show its utility on a number of test models, as well as its applications to modeling in virtual reality environments.