Image Aligning and Stitching Based on Multilayer Mesh Deformation

Abstract

This paper aims to solve the problem of strong disparity in image stitching. By studying current mesh deformation methods based on single grid, we propose an image stitching framework based on multilayer mesh deformation for aligning regions in different layer. With development of image depth perception and semantic segmentation technology, we can get layering maps of images or photos expediently. We introduce images representation with layers and get layer corresponding by using depth or disparity information for large parallax scenarios. Registration of each layer is carried out independently. To ensure the integrity of layer synthesis results, we apply deformation with translation and scaling compensation between different layers before blending. The experiment demonstrates that our method can adequately utilize the prior information in layering maps to decouple 2D transformation between different layers, finally achieve outstanding aligning performance in all layers and naturalness in complete stitching result.