UAV Image Stitching Based on Mesh-Guided Deformation and Ground Constraint
Quan Xu, Jun Chen, Linbo Luo, Wenping Gong, Yong Wang
Abstract
This article introduces a drone image stitching based on mesh-guided deformation and ground constraint, which can closely match the characteristics of images and achieve precise registration and acquire ideal stitching effect. The traditional methods use the homography model to align the image, which causes artifacts in the result of stitching the images with parallax. To overcome this situation, the image is divided into meshes and the mesh vertices of the target image are used to guide the warping. A new energy function is designed to represent the deformation characteristics of the image. We propose a new alignment term by using local homography and a local scale term by using the edge information of the mesh. The established mesh-guided deformation model can overcome image parallax caused by some external factors and eliminate the ghostly parts of the result. Moreover, imaged scene is not effectively planar and some fluctuations exist in the scene of the images, which will distort the stitching result. We propose a ground constraint with the ground plane as the main plane to reduce projection distortions in non-overlapping areas between images. Finally, the method of creating groundtruth is proposed, which can evaluate the naturalness of results and make comparison more reasonable. Several sets of challenging drone images are tested, and the experimental results show that our stitching system has good results.