Goesele, Michael and Snavely, Noah and Curless, Brian and Hoppe, Hugues and Seitz, Steven M. (2007):
Multi-View Stereo for Community Photo Collections.
IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brasil, October 14-20, 2007, [Conference or Workshop Item]
Abstract
We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structure-from-motion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate high-quality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multi-view stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2007 |
| Creators: | Goesele, Michael and Snavely, Noah and Curless, Brian and Hoppe, Hugues and Seitz, Steven M. |
| Title: | Multi-View Stereo for Community Photo Collections |
| Language: | English |
| Abstract: | We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structure-from-motion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate high-quality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multi-view stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet. |
| Uncontrolled Keywords: | Forschungsgruppe Capturing Reality (CARE), Computer vision, Multi-view stereo, Internet, Community photo collections, Depth maps |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Graphics, Capture and Massively Parallel Computing 20 Department of Computer Science > Interactive Graphics Systems |
| Event Title: | IEEE 11th International Conference on Computer Vision |
| Event Location: | Rio de Janeiro, Brasil |
| Event Dates: | October 14-20, 2007 |
| Date Deposited: | 16 Apr 2018 09:03 |
| Official URL: | http://download.hrz.tu-darmstadt.de/media/FB20/GCC/paper/Goe... |
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