Comparing salient point detectors
Pattern Recognition Letters
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Salient critical points for meshes
Proceedings of the 2007 ACM symposium on Solid and physical modeling
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Surface matching with salient keypoints in geodesic scale space
Computer Animation and Virtual Worlds - CASA'2008 Special Issue
A salient-point signature for 3d object retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Salient spectral geometric features for shape matching and retrieval
The Visual Computer: International Journal of Computer Graphics
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
International Journal of Computer Vision
A robust 3D interest points detector based on Harris operator
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'10 track: feature detection and description
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Local features for partial shape matching and retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Graph-based combinations of fragment descriptors for improved 3D Object Retrieval
Proceedings of the 3rd Multimedia Systems Conference
Key-component detection on 3D meshes using local features
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
A Performance Evaluation of Volumetric 3D Interest Point Detectors
International Journal of Computer Vision
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In this paper, we compare the results of five 3D interest point detection techniques to the interest points marked by human subjects. This comparison is used to quantitatively evaluate the interest point detection algorithms. We asked human subjects to look at a number of 3D models, and mark interest points on the models via a web-based interface. We propose a voting-based method to construct ground truth out of humans' selections of interest points. Evaluation measures, namely False Positive and False Negative Errors, are then defined based on the geodesic distance between the interest points detected by a particular algorithm and the human-generated ground truth.