Projects / Photographing long scenes with multi-viewpoint panoramas
Photographing long scenes with multi-viewpoint panoramas
Aseem Agarwala| Adobe Systems, University of Washington
Maneesh Agrawala| UC Berkeley
Michael Cohen| Microsoft Research
David Salesin| Adobe Systems, University of Washington
Rick Szeliski| Microsoft Research
We developed a system for producing multi-viewpoint panoramas of long, roughly planar scenes, such as the facades of buildings along a city street, from a relatively sparse set of photographs captured with a handheld still camera that is moved along the scene. Our work is a significant departure from previous methods for creating multi-viewpoint panoramas, which composite thin vertical strips from a video sequence captured by a translating video camera, in that the resulting panoramas are composed of relatively large regions of ordinary perspective. In our system, the only user input required beyond capturing the photographs themselves is to identify the dominant plane of the photographed scene; our system then computes a panorama automatically using Markov Random Field optimization. Users may exert additional control over the appearance of the result by drawing rough strokes that indicate various high-level goals. We demonstrate the results of our system on several scenes, including urban streets, a river bank, and a grocery store aisle.
Publications |
Photographing long scenes with multi-viewpoint panoramas
ACM Transactions on Graphics (Proc. SIGGRAPH), 25(3), 853–861