Trendy suggestions for learning-based elastic warps enable the deep image stitchings to align images exposed to large parallax errors. Despite the remarkable alignments, the methods struggle with occasional holes or discontinuity between overlapping and non-overlapping regions of a target image as the applied training strategy mostly focuses on overlap region alignment. As a result, they require additional modules such as seam finder and image inpainting for hiding discontinuity and filling holes, respectively. In this work, we suggest Recurrent Elastic Warps (REwarp) that address the problem with Dirichlet boundary condition and boost performances by residual learning for recurrent misalign correction. Specifically, REwarp predicts a homography and a Thin-plate Spline (TPS) under the boundary constraint for discontinuity and hole-free image stitching. Our experiments show the favorable aligns and the competitive computational costs of REwarp compared to the existing stitching methods. Our source code is available at https://github.com/minshu-kim/REwarp.

学习为基础的弹性扭曲的潮流趋势使得深度图像拼接能够对曝光差异较大的图像进行对齐。然而，这些方法在处理重叠区域和非重叠区域之间的间断时存在困难，因为应用的训练策略主要关注重叠区域的对齐，所以需要额外的模块来隐藏间断并填充空洞。本文提出了一种使用Dirichlet边界条件并通过残差学习进行循环误差校正的循环弹性扭曲（REwarp）方法，用于解决这个问题。具体而言，REwarp根据边界约束预测出一个单应性变换和一个Thin-plate Spline（TPS），用于实现无间断和无空洞的图像拼接。我们的实验证明了REwarp与现有拼接方法相比具有有利的对齐效果和竞争性的计算成本。我们的源代码可以在此https网址上获得。

基于迪利克雷边界条件学习图像拼接的剩余弹性编织