Curvature-based representation of objects from range data
Image and Vision Computing
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing and recognizing 3-D objects using surface properties
Describing and recognizing 3-D objects using surface properties
Inferring Surface Trace and Differential Structure from 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape representation and recognition from multiscale curvature
Computer Vision and Image Understanding
Dynamic Programming Generation of Curves on Brain Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
N-Dimensional Tensor Voting and Application to Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Difference Schemes for Edge Enhancing Beltrami Flow
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Photometric Computation of the Sign of Gaussian Curvature Using a Curve-Orientation Invariant
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Estimating the tensor of curvature of a surface from a polyhedral approximation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Curvature Estimation of Surfaces in 3D Grey-Value Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Iterated tensor voting and curvature improvement
Signal Processing
Computer Vision and Image Understanding
Robust principal curvatures on multiple scales
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Robust statistical estimation of curvature on discretized surfaces
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Principal curvatures from the integral invariant viewpoint
Computer Aided Geometric Design
Integral invariants for robust geometry processing
Computer Aided Geometric Design
Extracting lines of curvature from noisy point clouds
Computer-Aided Design
Curvature-aware adaptive re-sampling for point-sampled geometry
Computer-Aided Design
Robust Voronoi-based curvature and feature estimation
2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Robust and accurate curvature estimation using adaptive line integrals
EURASIP Journal on Advances in Signal Processing
GPU curvature estimation on deformable meshes
I3D '11 Symposium on Interactive 3D Graphics and Games
3D shape from unorganized 3d point clouds
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Curvature tensor computation by piecewise surface interpolation
Computer-Aided Design
| Hi-index | 0.16 |
Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.