Drone Image Stitching Using Local Mesh-Based Bundle Adjustment and Shape-Preserving Transform

This article proposes a strategy for drone image stitching using local mesh-based bundle adjustment and shape-preserving transform, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using a simple homography cannot handle the situation that the input drone images have parallax effect, and the image mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following steps. Starting from initial feature sets obtained by off-the-shelf feature extraction methods, we incorporate the parallax errors into an energy minimum framework and construct a robust alignment energy. This energy can be minimized efficiently based on local bundle adjustment and robust
$3\sigma $
principle, which could eliminate parallax effects and achieve accurate alignment. Then the seamless panoramic image is obtained by warping the target image and the source images onto the mesh plane directly. An image patch can be transformed by projective transformation (e.g., homography), which provides good alignment but may cause distortions. Consequently, combined with mesh-based shape-preserving transform, our proposed strategy can improve the naturalness of the results flexibly. Experiments show that our stitching strategy can eliminate parallax effects more effectively and achieve natural-looking results compared to other state-of-the-art methods.