Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Convex Optimization
Model Composition from Interchangeable Components
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Style-content separation by anisotropic part scales
ACM SIGGRAPH Asia 2010 papers
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Probabilistic reasoning for assembly-based 3D modeling
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
Co-Segmentation of 3D Shapes via Subspace Clustering
Computer Graphics Forum
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Part analogies in sets of objects
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization
Computer-Aided Design
| Hi-index | 0.00 |
Co-segmentation of 3D shapes in the same category is an intensive topic in computer graphics. In this paper, we present an unsupervised method to segment a set of meshes into corresponding parts in a consistent manner. Given the over-segmented patches as input, the co-segmentation result is generated by grouping them. In contrast to the previous method, we formulate the problem as a multi-view spectral clustering task by co-training a set of affinity matrices derived from different shape descriptors. For each shape descriptor, the affinity matrix is constructed via combining low-rankness and sparse representation. The integration of multiple features makes our method tolerate the large geometry and topology variations among the 3D meshes in a set. Moreover, the low-rank and sparse representation can capture not only the global structure but also the local relationship, which demonstrate robust to outliers. The experimental results show that our approach successfully segments each category in the benchmark dataset into corresponding parts and generates more reliable results compared with the state-of-the-art.