Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Bayesian Reconstruction of 3D Shapes and Scenes From A Single Image
HLK '03 Proceedings of the First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2005 Papers
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Single View Reconstruction of Curved Surfaces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Example Based 3D Reconstruction from Single 2D Images
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Plane-Based Optimization for 3D Object Reconstruction from Single Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Automatic Single-View 3-d Reconstruction of Urban Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we aim to reconstruct free-form 3D models from only one or few silhouettes by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: (1) a framework for learning the shape prior of the 3D objects, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects and (2) novel probabilistic inference schemes for automatically reconstructing 3D shapes from the silhouette(s) in the single view or sparse views. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach.