Name
Abstract | ||
|---|---|---|
We present a novel stereo algorithm which performs surface reconstruction from planar camera arrays. It incorporates the merits of both generic camera arrays and rectified binocular setups, recovering large surfaces like the former and performing efficient computations like the latter. First, we introduce a rectification algorithm which gives freedom in the design of camera arrays and simplifies photometric and geometric computations. We then define a novel set of data-fusion functions over 4-neighborhoods of cameras, which treat all cameras symmetrically and enable standard binocular stereo algorithms to handle arrays with arbitrary number of cameras. In particular, we introduce a photometric fusion function which handles partial visibility and extracts depth information along both horizontal and vertical baselines. Finally, we show that layered depth images and sprites with depth can be efficiently extracted from the rectified 3D space. Experimental results on real images confirm the effectiveness of the proposed method, which reconstructs dense surfaces larger by 20% on Tsukuba. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CVPR.2008.4587425 | CVPR |
Keywords | Field | DocType |
photometric computation,data-fusion functions,generic camera arrays,realistic images,image reconstruction,multiview stereo reconstruction,cameras,photometric fusion function,binocular stereo algorithms,surface reconstruction,stereo image processing,geometric computation,planar camera arrays,real images,sensor fusion,rectification algorithm,data fusion,optical imaging,photometry,computational modeling,data mining,pixel | Iterative reconstruction,Computer vision,Surface reconstruction,Stereo camera,Computer science,Sensor fusion,Planar,Artificial intelligence,Pixel,Real image,Computation | Conference |
Volume | Issue | ISSN |
2008 | 1 | 1063-6919 E-ISBN : 978-1-4244-2243-2 |
ISBN | Citations | PageRank |
978-1-4244-2243-2 | 6 | 0.50 |
References | Authors | |
23 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthieu Maitre | 1 | 123 | 10.32 |
| Yoshihisa Shinagawa | 2 | 1900 | 124.80 |
| Minh N. Do | 3 | 1681 | 133.55 |