High Precision Mesh-Based Drone Image Stitching Based on Salient Structure Preservation and Regular Boundaries

Abstract

Addressing problems such as obvious ghost, dislocation, and distortion resulting from the traditional stitching method, a novel drone image-stitching method is proposed using mesh-based local double-feature bundle adjustment and salient structure preservation which aims to obtain more natural panoramas.The proposed method is divided into the following steps. First, reducing parallax error is considered from both global and local aspects. Global bundle adjustment is introduced to minimize global transfer error, and then the local mesh-based feature-alignment model is incorporated into the optimization framework to achieve more accurate alignment. Considering the sensitivity of human eyes to linear structure, the global linear structure that runs through the images obtained by segment fusion is introduced to prevent distortions and align matching line segments better. Rectangular panoramas usually have better visual effects. Therefore, regular boundary constraint combined with mesh-based shape-preserving transform can make the results more natural while preserving mesh geometry. Two new evaluation metrics are also developed to quantify the performance of linear structure preservation and the alignment difference of matching line segments. Extensive experiments show that our proposed method can eliminate parallax and preserve global linear structures better than other state-of-the-art stitching methods and obtain more natural-looking stitching results.