Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
Accurate reconstruction of engineered models with surfaces of revolution
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Feature line extraction from unorganized noisy point clouds using truncated Fourier series
The Visual Computer: International Journal of Computer Graphics
SMI 2013: Voronoi-based feature curves extraction for sampled singular surfaces
Computers and 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
Spatial correlation of multi-sensor features for autonomous victim identification
Robot Soccer World Cup XV
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This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.