Automatic Multiperspective Images

Abstract

Multiperspective images generated from a collection of photographs or a videostream can be used to effectively summarize long, roughly planar scenes such as city streets. The final image will span a larger field of view than any single input image. However, common projections used to make these images, including cross-slits and pushbroom projections, may suffer from depth-related distortions in non-planar scenes. In this paper, we use an aspect-ratio distortion metric to compare these images to standard perspective projections. By minimizing this error metric we can automatically define the picture surface and viewpoints of a multiperspective image that reduces distortion artifacts. This optimization requires only a coarse estimate of scene geometry which can be provided as a depth map or a 2D spatial importance map defining interesting parts of the scene. These maps can be automatically constructed in most cases, allowing rapid generation of images of very long scenes.