Multi-view stereo for community photo collections
IEEE International Conference on Computer Vision (ICCV) 2007.
Detailed 3D models reconstructed from crawled Internet images.
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.
One application may be to provide useful 3D impostors to improve view interpolation for image-based
rendering systems like Photosynth
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