Using Extremal Boundaries for 3-D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Complete Dense Stereovision Using Level Set Methods
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Globally Minimal Surfaces by Continuous Maximal Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image-based multiresolution implicit object modeling
EURASIP Journal on Applied Signal Processing
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Global Optimization in Multiview 3D Reconstruction
International Journal of Computer Vision
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a novel framework for multi-view stereo that poses the problem of recovering a 3D surface in the scene as a regularized minimal partition problem of the visibility function in the presence of clutter. We introduce a simple and robust method to integrate estimates from several views that tolerates both static and time-varying clutter. Our formulation does not rely on the visual hull, 2D silhouettes, or make use of initial surface estimates. Furthermore, we use a globally optimal framework, so that the solution does not depend on initialization and computationally efficient numerical methods can be used to find the solution. We also strive for simplicity so that more general models of image formation can be used without compromising the estimation process. Experimental results on synthetic and publicly available real data show that our method performs on a par with state-of-the-art methods that have been used on clutter-free data.