Zhang, Yun, Lu, Yao, Yang, Jialing, Zhu, Zhe, Lai, Yu-Kun  ORCID: https://orcid.org/0000-0002-2094-5680, Zhang, Fang-Lue and Zheng, Xinyuan
2026.
Rectangling stitched images via unsupervised warping.
Visual Computer
42
(9)
, 388.
10.1007/s00371-026-04624-6
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (5MB) | Preview |
Abstract
Image stitching allows wide field-of-view images to be created. However, handheld shooting and alignment of overlapping regions in image stitching intrinsically result in irregular boundaries, compromising the wide-angle effect. To address this problem, we propose an unsupervised warping-based method for rectangling stitched images. We formulate irregular mesh prediction as a mesh motion regression task, constrained by three complementary objectives: shape-preserving, boundary-fitting, and content-preserving losses. This approach leverages geometric and semantic features of images to achieve rectangling without requiring labeled training data. Our primary contributions include (1) a label-free learning framework that improves rectification performance and generalization capability, and (2) a novel boundary-fitting scheme that reconstructs well-aligned meshes, producing visually natural rectangling results across diverse scenarios. Experiments demonstrate that our method achieves competitive or superior performance compared with state-of-the-art supervised methods.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Additional Information: | RRS applied |
| Publisher: | Springer |
| ISSN: | 0178-2789 |
| Date of First Compliant Deposit: | 10 July 2026 |
| Date of Acceptance: | 21 June 2026 |
| Last Modified: | 10 Jul 2026 09:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187916 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions