Algorithms for clustering data
Algorithms for clustering data
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multibody Structure and Motion: 3-D Reconstruction of Independently Moving Objects
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Reconstructing 3D trajectories of independently moving objects using generic constraints
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Accurate Motion Layer Segmentation and Matting
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Web-based 3D Reconstruction Service
Machine Vision and Applications
Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Generative Image Segmentation Using Random Walks with Restart
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Multibody structure-and-motion segmentation by branch-and-bound model selection
IEEE Transactions on Image Processing
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning Dense Optical-Flow Trajectory Patterns for Video Object Extraction
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On the shape of a set of points in the plane
IEEE Transactions on Information Theory
Representing moving images with layers
IEEE Transactions on Image Processing
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In this paper, we present a new framework for three-dimensional (3D) reconstruction of multiple rigid objects from dynamic scenes. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In our framework, we aim to reconstruct the 3D models of multiple objects in a more general setting where the configuration of the objects varies among views. We solve this problem by object-centered decomposition of the dynamic scenes using unsupervised co-recognition approach. Unlike conventional motion segmentation algorithms that require small motion assumption between consecutive views, co-recognition method provides reliable accurate correspondences of a same object among unordered and wide-baseline views. In order to segment each object region, we benefit from the 3D sparse points obtained from the structure-from-motion. These points are reliable and serve as automatic seed points for a seeded-segmentation algorithm. Experiments on various real challenging image sequences demonstrate the effectiveness of our approach, especially in the presence of abrupt independent motions of objects.