Method and apparatus for finding feature correspondences between images captured in real-world environments

Abstract

Techniques for computing a globally consistent set of image feature correspondences across a wide range of viewpoints suitable for interactive walkthroughs and visualizations. The inventive approach takes advantage of the redundancy inherent in a dense set of images captured in a plane (or in higher dimensions, e.g., images captured in a volume, images captured over time, etc). The technique may detect features in a set of source images and track the features to neighboring images. When features track to the same position in the same image, they are flagged as potential correspondences. Among the potential correspondences, the technique selects the maximal set using a greedy graph-labeling algorithm (e.g., best-first order). Only correspondences that produce a globally consistent labeling are selected. After globalization is done, a set of features common to a group of images can be quickly found and used to warp and combine the images to produce an interpolated novel view of the environment.