Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Pictorial Structures for Object Recognition
International Journal of Computer Vision
POP: Patchwork of Parts Models for Object Recognition
International Journal of Computer Vision
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Contextual Part Analogies in 3D Objects
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Style-content separation by anisotropic part scales
ACM SIGGRAPH Asia 2010 papers
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Exploration of continuous variability in collections of 3D shapes
ACM SIGGRAPH 2011 papers
Characterizing structural relationships in scenes using graph kernels
ACM SIGGRAPH 2011 papers
Joint shape segmentation with linear programming
Proceedings of the 2011 SIGGRAPH Asia Conference
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
Exploring collections of 3D models using fuzzy correspondences
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
A probabilistic model for component-based shape synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Fit and diverse: set evolution for inspiring 3D shape galleries
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Co-Segmentation of 3D Shapes via Subspace Clustering
Computer Graphics Forum
A search-classify approach for cluttered indoor scene understanding
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Acquiring 3D indoor environments with variability and repetition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
An optimization approach for extracting and encoding consistent maps in a shape collection
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Structure recovery by part assembly
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Attribit: content creation with semantic attributes
Proceedings of the 26th annual ACM symposium on User interface software and technology
Fine-grained semi-supervised labeling of large shape collections
ACM Transactions on Graphics (TOG)
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As large repositories of 3D shape collections continue to grow, understanding the data, especially encoding the inter-model similarity and their variations, is of central importance. For example, many data-driven approaches now rely on access to semantic segmentation information, accurate inter-model point-to-point correspondence, and deformation models that characterize the model collections. Existing approaches, however, are either supervised requiring manual labeling; or employ super-linear matching algorithms and thus are unsuited for analyzing large collections spanning many thousands of models. We propose an automatic algorithm that starts with an initial template model and then jointly optimizes for part segmentation, point-to-point surface correspondence, and a compact deformation model to best explain the input model collection. As output, the algorithm produces a set of probabilistic part-based templates that groups the original models into clusters of models capturing their styles and variations. We evaluate our algorithm on several standard datasets and demonstrate its scalability by analyzing much larger collections of up to thousands of shapes.