Multiple Combined Constraints for Image Stitching

TL;DR

This paper introduces a novel image stitching method that combines geometric and photometric constraints within a mesh-based framework, leading to improved alignment and robustness against large parallax.

Contribution

It proposes a combined approach that leverages the complementary strengths of geometric and photometric constraints for superior image stitching.

Findings

Outperforms state-of-the-art methods in alignment quality.

Handles larger parallax than photometric-only methods.

Effective in diverse and challenging scenes.

Abstract

Several approaches to image stitching use different constraints to estimate the motion model between image pairs. These constraints can be roughly divided into two categories: geometric constraints and photometric constraints. In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary. On the one hand, geometric constraints (e.g., point and line correspondences) are usually spatially biased and are insufficient in some extreme scenes, while photometric constraints are always evenly and densely distributed. On the other hand, photometric constraints are sensitive to displacements and are not suitable for images with large parallaxes, while geometric constraints are usually imposed by feature matching and are more robust to handle parallaxes. The proposed method therefore combines them together in an efficient mesh-based image warping framework. It achieves better alignment quality than methods only with geometric constraints, and can handle larger parallax than photometric-constraint-based method. Experimental results on various images illustrate that the proposed method outperforms representative state-of-the-art image stitching methods reported in the literature.