  Generating high-quality stitched images is a challenging task in computer
vision. The existing feature-based image stitching methods commonly only focus
on point and line features, neglecting the crucial role of higher-level planar
features in image stitching. This paper proposes an image stitching method
based on invariant planar features, which uses planar features as constraints
to improve the overall effect of natural image stitching. Initially, our
approach expands the quantity of matched feature points and lines through
straight-line procedures, advancing alignment quality and reducing artifacts in
overlapping areas. Then, uncertain planes are described by known matching
points and matching lines, and plane features are introduced to preserve energy
items, which improves the overall appearance of stitched images while reducing
distortion and guarantees a more natural stitched image. Furthermore, to
demonstrate the superiority of our approach, we also propose several evaluation
indexes related to planar features to quantify the detailed changes of planar
features. An extensive set of experiments validates the effectiveness of our
approach in stitching natural images with a larger field of view. Compared with
the most advanced methods, our method retains the texture and structure of the
image better, and produces less unnatural distortion. Multiple quantitative
evaluations illustrate that our approach outperforms existing methods with
significant improvements, further validating the effectiveness and superiority
of our proposed method.
