Parallax-tolerant Image Stitching via Segmentation-guided Multi-homography Warping
arxiv(2024)
摘要
Large parallax between images is an intractable issue in image stitching.
Various warping-based methods are proposed to address it, yet the results are
unsatisfactory. In this paper, we propose a novel image stitching method using
multi-homography warping guided by image segmentation. Specifically, we
leverage the Segment Anything Model to segment the target image into numerous
contents and partition the feature points into multiple subsets via the
energy-based multi-homography fitting algorithm. The multiple subsets of
feature points are used to calculate the corresponding multiple homographies.
For each segmented content in the overlapping region, we select its
best-fitting homography with the lowest photometric error. For each segmented
content in the non-overlapping region, we calculate a weighted combination of
the linearized homographies. Finally, the target image is warped via the
best-fitting homographies to align with the reference image, and the final
panorama is generated via linear blending. Comprehensive experimental results
on the public datasets demonstrate that our method provides the best alignment
accuracy by a large margin, compared with the state-of-the-art methods. The
source code is available at https://github.com/tlliao/multi-homo-warp.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要