Feature-preserving denoising of point-sampled surfaces
CEA'09 Proceedings of the 3rd WSEAS international conference on Computer engineering and applications
Robust denoising of point-sampled surfaces
WSEAS Transactions on Computers
Reconstructing sharp features of triangular meshes
Proceedings of the twenty-fifth annual symposium on Computational geometry
ℓ1-Sparse reconstruction of sharp point set surfaces
ACM Transactions on Graphics (TOG)
Extraction of the lines of curvature from raw point cloud
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
Visible neighborhood graph of point clouds
Graphical Models
Feature-Preserving Reconstruction of Singular Surfaces
Computer Graphics Forum
A survey of methods for moving least squares surfaces
SPBG'08 Proceedings of the Fifth Eurographics / IEEE VGTC conference on Point-Based Graphics
Feature line extraction from unorganized noisy point clouds using truncated Fourier series
The Visual Computer: International Journal of Computer Graphics
SMI 2013: Point cloud normal estimation via low-rank subspace clustering
Computers and Graphics
An adaptive normal estimation method for scanned point clouds with sharp features
Computer-Aided Design
Splatting lines: an efficient method for illustrating 3D surfaces and volumes
Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
Feature-Preserving Surface Reconstruction and Simplification from Defect-Laden Point Sets
Journal of Mathematical Imaging and Vision
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Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of Robust Moving Least Squares to locally fit surfaces to potential features. Using Newton's method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression.