Curvature-based representation of objects from range data
Image and Vision Computing
Trace Inference, Curvature Consistency, and Curve Detection
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
Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign of Gaussian Curvature From Curve Orientation in Photometric Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of Integrated Surface, Curve, and Junction Descriptions From Sparse 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Faithful Least-Squares Fitting of Spheres, Cylinders, Cones and Tori for Reliable Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Modelling Objects having Quadric Surfaces Incorporating Geometric cCnstraints
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Automatic, Accurate Surface Model Inference for Dental CAD/Cam
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Tensor voting in computer vision, visualization, and higher dimensional inferences
Tensor voting in computer vision, visualization, and higher dimensional inferences
Inference of Segmented Overlapping Surfaces from Binocular Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
First Order Augmentation to Tensor Voting for Boundary Inference and Multiscale Analysis in 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Adaptive Tensors of Curvature by Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sampling Framework for Accurate Curvature Estimation in Discrete Surfaces
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-squares-based fitting of paraboloids
Pattern Recognition
Iterated tensor voting and curvature improvement
Signal Processing
Computer Vision and Image Understanding
Integration of local and global geometrical cues for 3D face recognition
Pattern Recognition
Curvature Estimation and Curve Inference with Tensor Voting: A New Approach
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A clustering approach to free form surface reconstruction from multi-view range images
Image and Vision Computing
On the normal vector estimation for point cloud data from smooth surfaces
Computer-Aided Design
Robust and accurate curvature estimation using adaptive line integrals
EURASIP Journal on Advances in Signal Processing
A vectorial rotation-invariant 3-D shape descriptor
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Direct solutions for computing cylinders from minimal sets of 3d points
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
3D shape from unorganized 3d point clouds
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Recent methods for reconstructing surfaces from multiple images
IWMM'04/GIAE'04 Proceedings of the 6th international conference on Computer Algebra and Geometric Algebra with Applications
Field-guided registration for feature-conforming shape composition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Journal of Computational Physics
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We improve the basic tensor voting formalism to infer the sign and direction of principal curvatures at each input site from noisy 3D data. Unlike most previous approaches, no local surface fitting, partial derivative computation, nor oriented normal vector recovery is performed in our method. These approaches are known to be noise-sensitive since accurate partial derivative information is often required, which is usually unavailable from real data. Also, unlike approaches that detect signs of Gaussian curvature, we can handle points with zero Gaussian curvature uniformly, without first localizing them in a separate process. The tensor voting curvature estimation is noniterative, does not require initialization, and is robust to a considerable amount of outlier noise, as its effect is reduced by collecting a large number of tensor votes. Qualitative and quantitative results on synthetic and real, complex data are presented.