Reprojection Flow for Image Registration Across Seasons

We address the problem of robust visual data association across seasons and viewpoints. The predominant methods in this area are typically appearance–based, which lose representational power in outdoor and natural environments that have significant variation in appearance. After a natural environment is surveyed multiple times, we recover its 3D structure in a map, which provides the basis for robust data association. Our approach is called Reprojection Flow, which consists of using reprojected map points for appearance–invariant viewpoint selection and robust image registration. We evaluated this approach using a dataset of 24 surveys of a natural environment that span over a year. Experiments showed robustness to variation in appearance and viewpoint across seasons, a significant improvement over a state-of-the-art appearance–based technique for pairwise dense correspondence.