Towards Internet-scale Multi-view Stereo

This paper introduces an approach for enabling existing multi-view stereo methods to operate on extremely large unstructured photo collections. The main idea is to decompose the collection into a set of overlapping sets of photos that can be processed in parallel, and to merge the resulting reconstructions. This overlapping clustering problem is formulated as a constrained optimization and solved iteratively. The merging algorithm, designed to be parallel and out-of-core, incorporates robust filtering steps to eliminate low-quality reconstructions and enforce global visibility constraints. The approach has been tested on several large datasets downloaded from Flickr.com, including one with over ten thousand images, yielding a 3D reconstruction with nearly thirty million points.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Yasutaka Furukawa, Steve Seitz, Richard Szeliski
• Keywords: Computer Vision, CVPR 2010, Enforce Global Visibility, Large Unstructured Photo, Multi-view Stereo Methods