A deep multi-layer image stitching framework for parallax scenes

The smooth warp field is becoming increasingly important in image stitching. When the 3D scene is confronted, the relative positions of the scene structures observed at different camera views may differ, thus causing parallax problems. Existing image stitching algorithms often fail in parallax scenes and are not efficient enough to meet the requirements of real-time tasks such as panoramic stitching for UAVs. In this paper, we propose a deep multilayer image stitching framework for parallax scenes based on GPU parallel computing, which includes multi-scale RANSAC layers, a parallax suppression layer and a smooth projection field generation layer. Instead of strictly dividing the RANSAC process and the dense warping model into two steps as in traditional image warping, we use the dense warping model for outlier filtering in the RANSAC process, and the two steps are performed in a cross iterative fashion. Experimental results show that our algorithm significantly outperforms the existing algorithms in terms of computational performance and achieves an approximate level of quality of stitching.