Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Proceedings of the conference on Visualization '01
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Computing and Rendering Point Set Surfaces
IEEE Transactions on Visualization and Computer Graphics
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
Non-iterative, feature-preserving mesh smoothing
ACM SIGGRAPH 2003 Papers
Normal Improvement for Point Rendering
IEEE Computer Graphics and Applications
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Robust Feature Classification and Editing
IEEE Transactions on Visualization and Computer Graphics
Detection of closed sharp edges in point clouds using normal estimation and graph theory
Computer-Aided Design
Robust Smooth Feature Extraction from Point Clouds
SMI '07 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007
Parameterization-free projection for geometry reconstruction
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2007 papers
Triangulating point set surfaces with bounded error
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Data-dependent MLS for faithful surface approximation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Provably good moving least squares
ACM Transactions on Algorithms (TALG)
Spectral moving removal of non-isolated surface outlier clusters
Computer-Aided Design
Consolidation of unorganized point clouds for surface reconstruction
ACM SIGGRAPH Asia 2009 papers
Provable surface reconstruction from noisy samples
Computational Geometry: Theory and Applications
On the normal vector estimation for point cloud data from smooth surfaces
Computer-Aided Design
Comparison of surface normal estimation methods for range sensing applications
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
Sharp Feature Detection in Point Clouds
SMI '10 Proceedings of the 2010 Shape Modeling International Conference
Voronoi-Based Curvature and Feature Estimation from Point Clouds
IEEE Transactions on Visualization and Computer Graphics
Fast and Robust Normal Estimation for Point Clouds with Sharp Features
Computer Graphics Forum
Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
| Hi-index | 0.00 |
Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a robust normal estimation method is developed in order to effectively establish a proper neighborhood for each point in the scanned point cloud. In particular, for a point near sharp features, an anisotropic neighborhood is formed to only enclose neighboring points located on the same surface patch as the point. Neighboring points on the other surface patches are discarded. The developed method has been demonstrated to be robust towards noise and outliers in the scanned point cloud and capable of dealing with sparse point clouds. Some parameters are involved in the developed method. An automatic procedure is devised to adaptively evaluate the values of these parameters according to the varying local geometry. Numerous case studies using both synthetic and measured point cloud data have been carried out to compare the reliability and robustness of the proposed method against various existing methods.