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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ulusoy, A.O. Geiger, A. Black, M.J. |
| Copyright Year | 2015 |
| Description | Author affiliation: Max Planck Inst. for Intell. Syst., Tϋbingen, Germany (Ulusoy, A.O.; Geiger, A.; Black, M.J.) |
| Abstract | This paper presents a novel probabilistic foundation for volumetric 3D reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty. |
| Starting Page | 10 |
| Ending Page | 18 |
| File Size | 4871325 |
| Page Count | 9 |
| File Format | |
| e-ISBN | 9781467383325 |
| DOI | 10.1109/3DV.2015.9 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-19 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Three-dimensional displays Uncertainty Volumetric reconstruction Probabilistic logic Approximation algorithms Prediction algorithms Multi-view stereo Inference algorithms Ray potential Markov random field Image reconstruction |
| Content Type | Text |
| Resource Type | Article |
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