SMI '04 Proceedings of the Shape Modeling International 2004
Selecting Canonical Views for View-Based 3-D Object Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ACM SIGGRAPH 2005 Papers
Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Towards Stable and Salient Multi-View Representation of 3D Shapes
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Distinctive regions of 3D surfaces
ACM Transactions on Graphics (TOG)
Upright orientation of man-made objects
ACM SIGGRAPH 2008 papers
Theory and algorithm for learning with dissimilarity functions
Neural Computation
Technical Section: Semantics-driven best view of 3D shapes
Computers and Graphics
A Bayesian 3-D Search Engine Using Adaptive Views Clustering
IEEE Transactions on Multimedia
3D shape retrieval based on best view selection
Proceedings of the ACM workshop on 3D object retrieval
A benchmark for best view selection of 3D objects
Proceedings of the ACM workshop on 3D object retrieval
Enhancing online 3D products through crowdsourcing
Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
Best view selection of 3D models based on unsupervised feature learning and discrimination ability
Proceedings of the 6th International Symposium on Visual Information Communication and Interaction
SMI 2013: Grouping real functions defined on 3D surfaces
Computers and Graphics
ACM Transactions on Graphics (TOG)
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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We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes share the same salient features that discriminate them from the models of other classes. The main issue is learning these features. We propose a datadriven approach where the best view selection problem is formulated as a classification and feature selection problem; First a 3D model is described with a set of view-based descriptors, each one computed from a different viewpoint. Then a classifier is trained, in a supervised manner, on a collection of 3D models belonging to several shape categories. The classifier learns the set of 2D views that maximize the similarity between shapes of the same class and also the views that discriminate shapes of different classes. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark demonstrate the performance of the approach and its suitability for classification and online visual browsing of 3D data collections.