  Traditional image stitching approaches tend to leverage increasingly complex
geometric features (point, line, edge, etc.) for better performance. However,
these hand-crafted features are only suitable for specific natural scenes with
adequate geometric structures. In contrast, deep stitching schemes overcome the
adverse conditions by adaptively learning robust semantic features, but they
cannot handle large-parallax cases due to homography-based registration. To
solve these issues, we propose UDIS++, a parallax-tolerant unsupervised deep
image stitching technique. First, we propose a robust and flexible warp to
model the image registration from global homography to local thin-plate spline
motion. It provides accurate alignment for overlapping regions and shape
preservation for non-overlapping regions by joint optimization concerning
alignment and distortion. Subsequently, to improve the generalization
capability, we design a simple but effective iterative strategy to enhance the
warp adaption in cross-dataset and cross-resolution applications. Finally, to
further eliminate the parallax artifacts, we propose to composite the stitched
image seamlessly by unsupervised learning for seam-driven composition masks.
Compared with existing methods, our solution is parallax-tolerant and free from
laborious designs of complicated geometric features for specific scenes.
Extensive experiments show our superiority over the SoTA methods, both
quantitatively and qualitatively. The code is available at
https://github.com/nie-lang/UDIS2.
