利用深度图的自然图像拼接
Natural Image Stitching Using Depth Maps
AI总结：
本文提出利用深度图的自然图像拼接方法，通过稳健拟合和视图几何建立像素对应关系，生成更准确的对齐马赛克。
摘要：
自然图像拼接旨在从不同视角拍摄的相同3D场景的重叠图像中创建一个看起来自然的单一马赛克。当场景非平面且由手持相机捕捉时，不可避免地会出现挑战，因为在这种情况下视差不可忽略。在本文中，我们提出了一种新的图像拼接方法，利用深度图，以对抗视差生成准确的对齐马赛克。首先，我们构建了一种稳健的拟合方法，用于过滤特征匹配中的异常值，并估计输入图像之间的视图几何。然后，我们利用视图几何在输入图像之间建立像素到像素的对应关系，并使用所提出的最优变形来渲染变形图像。在渲染阶段，我们引入了几个模块来解决变形结果中的映射伪影，并生成最终的马赛克。在三个具有挑战性的数据集上的实验结果表明，输入图像的深度图使我们的方法在重叠区域提供了更加准确的对齐，并在非重叠区域提供了视点一致的结果。我们认为我们的方法将在单目深度估计快速发展的背景下继续有效。源代码可在https://github.com/tlliao/NIS_depths上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
