  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.
