MegaStitch: Robust Large-Scale Image Stitching Article Swipe
Ariyan Zarei
,
Emmanuel Gonzalez
,
Nirav Merchant
,
Duke Pauli
,
Eric Lyons
,
Kobus Barnard
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tgrs.2022.3141907
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tgrs.2022.3141907
We address fast image stitching for large image collections while being robust to drift due to chaining transformations and minimal overlap between images. We focus on scientific applications where ground-truth accuracy is far more\nimportant than visual appearance or projection error, which can be misleading. For common large-scale image stitching use cases, transformations between images are often restricted to similarity or translation. When homography is used in these cases, the odds of being trapped in a poor local minimum and producing unnatural results increases. Thus, for transformations up to affine, we cast stitching as minimizing reprojection error globally using linear least-squares with a few, simple constraints. For homography, we observe that the global affine solution provides better initialization for bundle adjustment compared to an alternative that initializes with a homography-based scaffolding and at lower computational cost. We evaluate our methods on a\nvery large translation dataset with limited overlap as well as four drone datasets. We show that our approach is better compared to alternative methods such as MGRAPH in terms of computational cost, scaling to large numbers of images, and robustness to drift. We also contribute ground-truth datasets for this endeavor.
Related Topics
Concepts
Image stitching
Computer science
Initialization
Robustness (evolution)
Bundle adjustment
Artificial intelligence
RANSAC
Ground truth
Affine transformation
Computer vision
Homography
Image registration
Image (mathematics)
Mathematics
Pure mathematics
Projective test
Chemistry
Projective space
Programming language
Gene
Biochemistry
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tgrs.2022.3141907
- https://ieeexplore.ieee.org/ielx7/36/9633014/09676637.pdf
- OA Status
- bronze
- Cited By
- 16
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206536538
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4206536538Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tgrs.2022.3141907Digital Object Identifier
- Title
-
MegaStitch: Robust Large-Scale Image StitchingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Ariyan Zarei, Emmanuel Gonzalez, Nirav Merchant, Duke Pauli, Eric Lyons, Kobus BarnardList of authors in order
- Landing page
-
https://doi.org/10.1109/tgrs.2022.3141907Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/36/9633014/09676637.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/36/9633014/09676637.pdfDirect OA link when available
- Concepts
-
Image stitching, Computer science, Initialization, Robustness (evolution), Bundle adjustment, Artificial intelligence, RANSAC, Ground truth, Affine transformation, Computer vision, Homography, Image registration, Image (mathematics), Mathematics, Pure mathematics, Projective test, Chemistry, Projective space, Programming language, Gene, Biochemistry, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 6, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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