Efficient computation of scale-space features for deformable shape correspondences
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images
International Journal of Computer Vision
3D Geometric Scale Variability in Range Images: Features and Descriptors
International Journal of Computer Vision
Ricci flow-based spherical parameterization and surface registration
Computer Vision and Image Understanding
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This paper formalizes a novel, intrinsic geometric scale space (IGSS) of 3D surface shapes. The intrinsic geometry of a surface is diffused by means of the Ricci flow for the generation of a geometric scale space. We rigorously prove that this multiscale shape representation satisfies the axiomatic causality property. Within the theoretical framework, we fur ther present a feature-based shape representation derived from IGSS processing, which is shown to be theoretically plausible and practically effective. By integrating the concept of scale-dependent saliency into the shape description, this representation is not only highly descriptive of the local structures, but also exhibits several desired characteristics of global shape representations, such as being compact, robust to noise and computationally efficient. We demonstrate the capabilities of our approach through salient geometric feature detection and highly discriminative matching of 3D scans.