Distinctive regions of 3D surfaces
ACM Transactions on Graphics (TOG)
Partial matching of 3D shapes with priority-driven search
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
A boosting approach to content-based 3D model retrieval
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
Component based shape retrieval using differential profiles
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Symmetric reconstruction algorithms for incomplete 3D models
International Journal of Computer Applications in Technology
Digital anastylosis of the Octagon in Ephesos
Journal on Computing and Cultural Heritage (JOCCH)
A Bag of Words Approach for 3D Object Categorization
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Supervised learning of similarity measures for content-based 3D model retrieval
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Volumetric heat kernel signatures
Proceedings of the ACM workshop on 3D object retrieval
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Semantic 3D Media and Content: Surface partial matching and application to archaeology
Computers and Graphics
Visual vocabulary signature for 3D object retrieval and partial matching
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
A robust 3D interest points detector based on Harris operator
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Semantics-driven approach for automatic selection of best views of 3D shapes
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Feature selection for enhanced spectral shape comparison
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
Heat diffusion approach for feature-based body scans analysis
EG 3DOR'11 Proceedings of the 4th 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
Extended cone-curvature based salient points detection and 3D model retrieval
Multimedia Tools and Applications
3D point of interest detection via spectral irregularity diffusion
The Visual Computer: International Journal of Computer Graphics
Local signature quantization by sparse coding
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
Learning kernels on extended Reeb graphs for 3d shape classification and retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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Databases of 3D shapes have become widespread for a variety of applications, and a key research problem is searching these databases for similar shapes. This paper introduces a method for finding distinctive features of a shape that are useful for determining shape similarity. Although global shape descriptors have been developed to facilitate retrieval, they fail when local shape properties are the distinctive features of a class. Alternatively, local shape descriptors can be generated over the surface of shapes, but then storage and search of the descriptors becomes unnecessarily expensive, as perhaps only a few descriptors are sufficient to distinguish classes. The challenge is to select local descriptors from a query shape that are most distinctive for retrieval. Our approach is to define distinction as the retrieval performance of a local shape descriptor. During a training phase, we estimate descriptor likelihood using a multivariate Gaussian distribution of real-valued shape descriptors, evaluate the retrieval performance of each descriptor from a training set, and average these performance values at every likelihood value. For each query, we evaluate the likelihood of local shape descriptors on its surface and lookup the expected retrieval values learned from the training set to determine their predicted distinction values. We show that querying with the most distinctive shape descriptors provides favorable retrieval performance during tests with a database of common graphics objects.