Research on Image Stitching Based on Invariant Features of Reconstructed Plane

TL;DR

This paper introduces a novel image stitching method leveraging invariant planar features to enhance alignment, reduce distortion, and produce more natural stitched images, outperforming existing techniques in quality and structural preservation.

Contribution

The paper presents a new approach that incorporates planar features into image stitching, expanding feature matching and introducing evaluation indexes for planar features, improving naturalness and accuracy.

Findings

Outperforms existing methods in preserving texture and structure.

Reduces unnatural distortion in stitched images.

Achieves significant improvements in quantitative evaluations.

Abstract

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.