Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Automatic reconstruction of surfaces and scalar fields from 3D scans
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Fitting smooth surfaces to dense polygon meshes
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Surface reconstruction with anisotropic density-scaled alpha shapes
Proceedings of the conference on Visualization '98
Implicit fairing of irregular meshes using diffusion and curvature flow
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Feature sensitive surface extraction from volume data
Proceedings of the 28th 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
Using Growing Cell Structures for Surface Reconstruction
SMI '03 Proceedings of the Shape Modeling International 2003
Adaptive Reconstruction of Freeform Objects with 3D SOM Neural Network Grids
PG '01 Proceedings of the 9th Pacific Conference on Computer Graphics and Applications
A Bayesian method for probable surface reconstruction and decimation
ACM Transactions on Graphics (TOG)
| Hi-index | 0.00 |
We present a method for the adaptive reconstruction of a surface directly from an unorganized point cloud. The algorithm is based on an incrementally expanding Neural Network and the statistical analysis of its Learning process. In particular, we make use of the simple observation that during the Learning process the normal of a vertex near a sharp edge or a high curvature area of the target space, statistically, will vary more than the normal of a vertex near a flat area. We use the information obtained from the study of these normal variations to steer the Learning process in an adaptive meshing application, producing meshes with more triangles near the high curvature areas. The same information is used in a feature detection application.