Abstract

The choice of motion models is vital in applications like image/video stitching and video stabilization. Conventional methods explored different approaches ranging from simple global parametric models to complex per-pixel optical flflow. Mesh-based warping methods achieve a good balance between computational complexity and model flflexibility. However, they typically require high quality feature correspondences and suffer from mismatches and low-textured image content. In this paper, we propose a mesh-based photometric alignment method that minimizes pixel intensity difference instead of Euclidean distance of known feature correspondences. The proposed method combines the superior performance of dense photometric alignment with the effificiency of mesh-based image warping. It achieves better global alignment quality than the feature-based counterpart in textured images, and more importantly, it is also robust to low-textured image content. Abundant experiments show that our method can handle a variety of images and videos, and outperforms representative state-of-the-art methods in both image stitching and video stabilization tasks