Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
PMR: point to mesh rendering, a feature-based approach
Proceedings of the conference on Visualization '02
Computing and Rendering Point Set Surfaces
IEEE Transactions on Visualization and Computer Graphics
Normal vector voting: crease detection and curvature estimation on large, noisy meshes
Graphical Models - Special issue: Processing on large polygonal meshes
Estimating surface normals in noisy point cloud data
Proceedings of the nineteenth annual symposium on Computational geometry
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
Non-iterative, feature-preserving mesh smoothing
ACM SIGGRAPH 2003 Papers
Crest Lines for Surface Segmentation and Flattening
IEEE Transactions on Visualization and Computer Graphics
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning (Synthesis Lectures on Image, Video, and Multimedia Processing)
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
Voronoi-based variational reconstruction of unoriented point sets
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Feature detection of triangular meshes based on tensor voting theory
Computer-Aided Design
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
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
Geometric Inference for Probability Measures
Foundations of Computational Mathematics
On normals and projection operators for surfaces defined by point sets
SPBG'04 Proceedings of the First Eurographics 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: Feature-aware filtering for point-set surface denoising
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SMI 2013: Point cloud normal estimation via low-rank subspace clustering
Computers and Graphics
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Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise, outliers and artifacts. Our method first takes the voting tensors at every point using the corresponding neighborhoods and computes the feature weight to infer the local structure via eigenvalue analysis of the tensor. The optimal scale for a point is automatically determined by observing the feature weight variation in order to deal with both a noisy smooth region and a sharp edge. We finally extract the points at sharp features using adaptive thresholding of the feature weight and the feature completion process. The multi-scale tensor voting of a given point set improves noise sensitivity and scale dependency of an input model. We demonstrate the strength of the proposed method in terms of efficiency and robustness by comparing it with other feature detection algorithms.