Natural Image Stitching Using Depth Maps
This addresses the challenge of creating seamless mosaics from handheld camera images for applications like photography and computer vision, representing a novel method for a known bottleneck.
The paper tackles the problem of natural image stitching for non-planar scenes with parallax by proposing a method using depth maps, achieving more accurate alignment in overlapping regions and view-consistent results in non-overlapping regions as demonstrated on three challenging datasets.
Natural image stitching aims to create a single, natural-looking mosaic from overlapped images that capture the same 3D scene from different viewing positions. Challenges inevitably arise when the scene is non-planar and captured by handheld cameras since parallax is non-negligible in such cases. In this paper, we propose a novel image stitching method using depth maps, which generates accurate alignment mosaics against parallax. Firstly, we construct a robust fitting method to filter out the outliers in feature matches and estimate the epipolar geometry between input images. Then, we utilize epipolar geometry to establish pixel-to-pixel correspondences between the input images and render the warped images using the proposed optimal warping. In the rendering stage, we introduce several modules to solve the mapping artifacts in the warping results and generate the final mosaic. Experimental results on three challenging datasets demonstrate that the depth maps of input images enable our method to provide much more accurate alignment in the overlapping region and view-consistent results in the non-overlapping region. We believe our method will continue to work under the rapid progress of monocular depth estimation. The source code is available at https://github.com/tlliao/NIS_depths.