A salient-point signature for 3d object retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
3D Line Drawing for Archaeological Illustration
International Journal of Computer Vision
Crease detection on noisy meshes via probabilistic scale selection
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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3D shape features are inherently scale-dependent. For instance, on a 3D model of a human body, the top of the head and a fingertip can both be detected as corner points, however, at entirely different scales. In this paper, we present a method for extracting and integrating 3D shape features in the discrete scale-space of a triangular mesh model. We first parameterize the surface of the mesh model on a 2D plane and then construct a dense surface normal map. In general, the parametrization is not isometric. To account for this, we compute the relative stretch of the original edge lengths. Next, we compute a dense distortion map which is used to approximate the geodesic distances on the normal map. Then, we construct a discrete scale-space of the original 3D shape by successively convolving the normal map with distortion-adapted Gaussian kernels of increasing standard deviation. We derive corner and edge detectors to extract 3D features at each scale in the discrete scale-space. Furthermore, we show how to combine the detector responses from different scales to form a unified representation of the 3D features.