Randomized Global Transformation Approach for Dense Correspondence

This paper describes a randomized global transformation approach to estimate dense correspondence for image pairs taken under challengingly different photometric and geometric conditions. Our approach assumes that a correspondence field consists of piecewise parametric transformation model. While conventional approaches consider large search space including flow and geometric fields exhaustively, our approach is based on an inference of optimal global transformation model from transformation candidates. To build a reliable global transformation hypothesis, we build optimal global transformation candidates with a randomized manner from an initial sparse feature correspondence, followed by a transformation clustering. Furthermore, the optimal global transformation is estimated as a cost filtering scheme with fast edge-aware filtering to provide a geometrical smoothness. Experiments demonstrate outstanding performance of our approach in terms of correspondence accuracy and computational complexity.