RecStitchNet: Learning to stitch images with rectangular boundaries Article Swipe
Yun Zhang
,
Yu‐Kun Lai
,
Lang Nie
,
Fang‐Lue Zhang
,
Lin Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s41095-024-0420-6
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s41095-024-0420-6
Irregular boundaries in image stitching naturally occur due to freely moving cameras. To deal with this problem, existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit solution. However, previous methods always depend on hand-crafted features (e.g., keypoints and line segments). Thus, failures often happen in overlapping regions without distinctive features. In this paper, we address this problem by proposing RecStitchNet, a reasonable and effective network for image stitching with rectangular boundaries. Considering that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework, we propose a three-step progressive learning based strategy, which not only simplifies this task, but gradually achieves a good balance between stitching and imposing rectangularity. In the first step, we perform initial stitching by a pre-trained state-of-the-art image stitching model, to produce initially warped stitching results without considering the boundary constraint. Then, we use a regression network with a comprehensive objective regarding mesh, perception, and shape to further encourage the stitched meshes to have rectangular boundaries with high content fidelity. Finally, we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively, which can effectively improve the stitching results in terms of feature alignment, as well as boundary and structure preservation. Due to the lack of stitching datasets and the difficulty of label generation, we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels, and the performance upper bound induced from the it can be broken by our unsupervised refinement. Qualitative and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s41095-024-0420-6
- https://link.springer.com/content/pdf/10.1007/s41095-024-0420-6.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401377613
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401377613Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s41095-024-0420-6Digital Object Identifier
- Title
-
RecStitchNet: Learning to stitch images with rectangular boundariesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-01Full publication date if available
- Authors
-
Yun Zhang, Yu‐Kun Lai, Lang Nie, Fang‐Lue Zhang, Lin XuList of authors in order
- Landing page
-
https://doi.org/10.1007/s41095-024-0420-6Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s41095-024-0420-6.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s41095-024-0420-6.pdfDirect OA link when available
- Concepts
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Image stitching, Computer science, Artificial intelligence, Polygon mesh, Image warping, Computer vision, Feature (linguistics), Boundary (topology), Computer graphics (images), Mathematics, Linguistics, Mathematical analysis, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2520293138, https://openalex.org/W4234552385, https://openalex.org/W3095840465, https://openalex.org/W3147619802, https://openalex.org/W4382450003, https://openalex.org/W4312275356, https://openalex.org/W3175249881, https://openalex.org/W2095149291, https://openalex.org/W2999589591, https://openalex.org/W4376316969, https://openalex.org/W2126060993, https://openalex.org/W1979076920, https://openalex.org/W2028045756, https://openalex.org/W2518764509, https://openalex.org/W2585881402, https://openalex.org/W2786533959, https://openalex.org/W4221094064, https://openalex.org/W2790280905, https://openalex.org/W2027179862, https://openalex.org/W2526468814, https://openalex.org/W3209371570, https://openalex.org/W2623012778, https://openalex.org/W2021968989, https://openalex.org/W2883327485, https://openalex.org/W2742959148, https://openalex.org/W2902346020, https://openalex.org/W4283270654, https://openalex.org/W2913059114, https://openalex.org/W4301409532, https://openalex.org/W3175551454, https://openalex.org/W3105304426, https://openalex.org/W4236965008 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 66, 93, 108, 125, 145, 149, 221 |
| abstract_inverted_index.In | 56, 116 |
| abstract_inverted_index.To | 12 |
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| abstract_inverted_index.as | 197, 199, 228 |
| abstract_inverted_index.be | 241 |
| abstract_inverted_index.by | 63, 124, 243 |
| abstract_inverted_index.in | 2, 50, 87, 192 |
| abstract_inverted_index.it | 239 |
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| abstract_inverted_index.on | 20, 38 |
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| abstract_inverted_index.we | 59, 91, 120, 143, 172, 217 |
| abstract_inverted_index.Due | 204 |
| abstract_inverted_index.and | 43, 68, 81, 113, 155, 201, 211, 231, 248, 251 |
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| abstract_inverted_index.but | 105 |
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| abstract_inverted_index.our | 244, 257 |
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| abstract_inverted_index.Then, | 142 |
| abstract_inverted_index.Thus, | 46 |
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| abstract_inverted_index.focus | 19 |
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| abstract_inverted_index.mesh, | 153 |
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| abstract_inverted_index.tasks | 86 |
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| abstract_inverted_index.upper | 234 |
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| abstract_inverted_index.freely | 9 |
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| abstract_inverted_index.images | 227 |
| abstract_inverted_index.meshes | 162, 183 |
| abstract_inverted_index.method | 258 |
| abstract_inverted_index.model, | 130 |
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| abstract_inverted_index.warped | 134 |
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| abstract_inverted_index.dataset | 223 |
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| abstract_inverted_index.improve | 188 |
| abstract_inverted_index.induced | 236 |
| abstract_inverted_index.initial | 122 |
| abstract_inverted_index.labels, | 230 |
| abstract_inverted_index.methods | 18, 35 |
| abstract_inverted_index.network | 70, 147 |
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| abstract_inverted_index.problem | 62 |
| abstract_inverted_index.produce | 132 |
| abstract_inverted_index.propose | 92, 173, 218 |
| abstract_inverted_index.regions | 52 |
| abstract_inverted_index.regular | 27 |
| abstract_inverted_index.results | 136, 191, 250 |
| abstract_inverted_index.warping | 23 |
| abstract_inverted_index.without | 53, 137 |
| abstract_inverted_index.Finally, | 171 |
| abstract_inverted_index.However, | 33 |
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| abstract_inverted_index.boundary | 140, 200 |
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| abstract_inverted_index.datasets | 210 |
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| abstract_inverted_index.failures | 47 |
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| abstract_inverted_index.generate | 220 |
| abstract_inverted_index.imposing | 82, 114 |
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| abstract_inverted_index.regarding | 152 |
| abstract_inverted_index.solution. | 32 |
| abstract_inverted_index.stitching | 4, 73, 80, 112, 123, 129, 135, 190, 209, 222 |
| abstract_inverted_index.strategy, | 98 |
| abstract_inverted_index.structure | 202 |
| abstract_inverted_index.advantages | 255 |
| abstract_inverted_index.alignment, | 196 |
| abstract_inverted_index.boundaries | 1, 26, 166 |
| abstract_inverted_index.difficulty | 213 |
| abstract_inverted_index.framework, | 90 |
| abstract_inverted_index.optimizing | 21 |
| abstract_inverted_index.reasonable | 67 |
| abstract_inverted_index.regression | 146 |
| abstract_inverted_index.segments). | 45 |
| abstract_inverted_index.simplifies | 102 |
| abstract_inverted_index.three-step | 94 |
| abstract_inverted_index.Considering | 77 |
| abstract_inverted_index.Qualitative | 247 |
| abstract_inverted_index.boundaries. | 76 |
| abstract_inverted_index.considering | 138 |
| abstract_inverted_index.constraint. | 141 |
| abstract_inverted_index.demonstrate | 253 |
| abstract_inverted_index.distinctive | 54 |
| abstract_inverted_index.effectively | 187 |
| abstract_inverted_index.evaluations | 252 |
| abstract_inverted_index.generation, | 216 |
| abstract_inverted_index.non-trivial | 85 |
| abstract_inverted_index.overlapping | 51 |
| abstract_inverted_index.perception, | 154 |
| abstract_inverted_index.performance | 233 |
| abstract_inverted_index.pre-trained | 126 |
| abstract_inverted_index.progressive | 95 |
| abstract_inverted_index.rectangular | 75, 165, 225 |
| abstract_inverted_index.refinement. | 246 |
| abstract_inverted_index.traditional | 30 |
| abstract_inverted_index.hand-crafted | 39 |
| abstract_inverted_index.iteratively, | 184 |
| abstract_inverted_index.optimization | 177 |
| abstract_inverted_index.quantitative | 249 |
| abstract_inverted_index.unsupervised | 175, 245 |
| abstract_inverted_index.RecStitchNet, | 65 |
| abstract_inverted_index.comprehensive | 150 |
| abstract_inverted_index.instance-wise | 176 |
| abstract_inverted_index.preservation. | 203 |
| abstract_inverted_index.learning-based | 89 |
| abstract_inverted_index.rectangularity | 83 |
| abstract_inverted_index.rectangularity. | 115 |
| abstract_inverted_index.state-of-the-art | 127 |
| abstract_inverted_index.state-of-the-art. | 261 |
| abstract_inverted_index.pseudo-ground-truth | 229 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100356849 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210141176 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.85905454 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |