SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Spacetime faces: high resolution capture for modeling and animation
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Surface matching with salient keypoints in geodesic scale space
Computer Animation and Virtual Worlds - CASA'2008 Special Issue
Geodesic Distance-weighted Shape Vector Image Diffusion
IEEE Transactions on Visualization and Computer Graphics
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Intrinsic Geometric Scale Space by Shape Diffusion
IEEE Transactions on Visualization and Computer Graphics
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids
Computer-Aided Design
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds
International Journal of Computer Vision
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With the rapid development of fast data acquisition techniques, 3D scans that record the geometric and photometric information of deformable objects are routinely acquired nowadays. To track surfaces in temporal domain or stitch partially-overlapping scans to form a complete model in spatial domain, robust and efficient feature detection for deformable shape correspondences, as an enabling method, becomes fundamentally critical with pressing needs. In this paper, we propose an efficient method to extract local features in scale spaces of both texture and geometry for deformable shape correspondences. We first build a hierarchical scale space on surface geometry based on geodesic metric, and the pyramid representation of surface geometry naturally engenders the rapid computation of scale-space features. Analogous to the SIFT, our features are found as local extrema in the scale space. We then propose a new feature descriptor for deformable surfaces, which is a gradient histogram within a local region computed by a local parameterization. Both the detector and the descriptor are invariant to isometric deformation, which makes our method a powerful tool for deformable shape correspondences. The performance of the proposed method is evaluated by feature matching on a sequence of deforming surfaces with ground truth correspondences.