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
Local features for partial shape matching and retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A robust 3D interest points detector based on Harris operator
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Learning the compositional structure of man-made objects for 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Evaluation of 3D interest point detection techniques
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Computer Vision and Image Understanding
3D point of interest detection via spectral irregularity diffusion
The Visual Computer: International Journal of Computer Graphics
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This paper develops a new salient keypoints-based shape description which extracts the salient surface keypoints with detected scales. Salient geometric features can then be defined collectively on all the detected scale normalized local patches to form a shape descriptor for surface matching purpose. The saliency-driven keypoints are computed as local extrema of the difference of Gaussian function defined over a curved surface in geodesic scale space. This method can properly function on either manifold or non-manifold surface without resorting to any surface mapping or parameterization procedures. Therefore, it has a wide utility in many applications such as shape matching, classification, and recognition. Our experiments on 3D shapes demonstrate that the salient keypoints and local feature descriptors are robust and stable to noisy input and insensitive to resolution change. We have applied our technique to the tasks of 3D shape matching, and the experimental results showed good performance and the effectiveness of this new method. Copyright © 2008 John Wiley & Sons, Ltd.