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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Teney, D. Piater, J. |
| Copyright Year | 2012 |
| Abstract | This paper introduces a novel method for feature-based 3D reconstruction using multiple calibrated 2D views. We use a probabilistic formulation of the problem in the 3D, reconstructed space that allows using features that cannot be matched one-to-one, or which cannot be precisely located, such as points along edges. The reconstructed scene, modelled as a probability distribution in the 3D space, is defined as the intersection of all reconstructions compatible with each available view. We introduce a method based on importance sampling to retrieve individual samples from that distribution, as well as an iterative method to identify contiguous regions of high density. This allows the reconstruction of continuous 3D curves compatible with all the given input views, without establishing specific correspondences and without relying on connectivity in the input images, while accounting for uncertainty in the input observations, due e.g.\ to noisy images and poorly calibrated cameras. The technical formulation is attractive in its flexibility and genericity. The implemented system, evaluated on several very different publicly-available datasets, shows results competitive with existing methods, effectively dealing with arbitrary numbers of views, wide baselines and imprecise camera calibrations. |
| Starting Page | 160 |
| Ending Page | 167 |
| File Size | 843984 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467344708 |
| e-ISBN | 9780769548739 |
| DOI | 10.1109/3DIMPVT.2012.28 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-13 |
| Publisher Place | Switzerland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Surface reconstruction Image edge detection Cameras Feature extraction Proposals Kernel Image reconstruction |
| Content Type | Text |
| Resource Type | Article |
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