Improving Facade Parsing with Vision Transformers and Line Integration Article Swipe
Bowen Wang
,
Jiaxing Zhang
,
Ran Zhang
,
Yunqin Li
,
Liangzhi Li
,
Yuta Nakashima
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15523
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15523
Facade parsing stands as a pivotal computer vision task with far-reaching applications in areas like architecture, urban planning, and energy efficiency. Despite the recent success of deep learning-based methods in yielding impressive results on certain open-source datasets, their viability for real-world applications remains uncertain. Real-world scenarios are considerably more intricate, demanding greater computational efficiency. Existing datasets often fall short in representing these settings, and previous methods frequently rely on extra models to enhance accuracy, which requires much computation cost. In this paper, we introduce Comprehensive Facade Parsing (CFP), a dataset meticulously designed to encompass the intricacies of real-world facade parsing tasks. Comprising a total of 602 high-resolution street-view images, this dataset captures a diverse array of challenging scenarios, including sloping angles and densely clustered buildings, with painstakingly curated annotations for each image. We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP). This marks the pioneering utilization of Vision Transformers (ViT) in facade parsing, and our experimental results definitively substantiate its merit. We also design Line Acquisition, Filtering, and Revision (LAFR), an efficient yet accurate revision algorithm that can improve the segment result solely from simple line detection using prior knowledge of the facade. In ECP 2011, RueMonge 2014, and our CFP, we evaluate the superiority of our method.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.15523
- https://arxiv.org/pdf/2309.15523
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387209455
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- OpenAlex ID
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https://openalex.org/W4387209455Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.15523Digital Object Identifier
- Title
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Improving Facade Parsing with Vision Transformers and Line IntegrationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-27Full publication date if available
- Authors
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Bowen Wang, Jiaxing Zhang, Ran Zhang, Yunqin Li, Liangzhi Li, Yuta NakashimaList of authors in order
- Landing page
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https://arxiv.org/abs/2309.15523Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2309.15523Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.15523Direct OA link when available
- Concepts
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Facade, Parsing, Computer science, Artificial intelligence, Transformer, Computation, Machine learning, Computer vision, Programming language, Engineering, Electrical engineering, Structural engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithm | 178 |
| abstract_inverted_index.clustered | 123 |
| abstract_inverted_index.datasets, | 36 |
| abstract_inverted_index.demanding | 50 |
| abstract_inverted_index.detection | 189 |
| abstract_inverted_index.efficient | 174 |
| abstract_inverted_index.encompass | 93 |
| abstract_inverted_index.including | 118 |
| abstract_inverted_index.introduce | 83, 133 |
| abstract_inverted_index.knowledge | 192 |
| abstract_inverted_index.planning, | 17 |
| abstract_inverted_index.scenarios | 45 |
| abstract_inverted_index.settings, | 62 |
| abstract_inverted_index.viability | 38 |
| abstract_inverted_index.Comprising | 101 |
| abstract_inverted_index.Filtering, | 169 |
| abstract_inverted_index.Real-world | 44 |
| abstract_inverted_index.buildings, | 124 |
| abstract_inverted_index.frequently | 66 |
| abstract_inverted_index.impressive | 31 |
| abstract_inverted_index.intricate, | 49 |
| abstract_inverted_index.pioneering | 147 |
| abstract_inverted_index.real-world | 40, 97 |
| abstract_inverted_index.scenarios, | 117 |
| abstract_inverted_index.uncertain. | 43 |
| abstract_inverted_index.Transformer | 140 |
| abstract_inverted_index.annotations | 128 |
| abstract_inverted_index.challenging | 116 |
| abstract_inverted_index.computation | 77 |
| abstract_inverted_index.efficiency. | 20, 53 |
| abstract_inverted_index.intricacies | 95 |
| abstract_inverted_index.open-source | 35 |
| abstract_inverted_index.street-view | 107 |
| abstract_inverted_index.superiority | 207 |
| abstract_inverted_index.utilization | 148 |
| abstract_inverted_index.Acquisition, | 168 |
| abstract_inverted_index.Transformers | 151 |
| abstract_inverted_index.applications | 11, 41 |
| abstract_inverted_index.considerably | 47 |
| abstract_inverted_index.definitively | 160 |
| abstract_inverted_index.experimental | 158 |
| abstract_inverted_index.far-reaching | 10 |
| abstract_inverted_index.meticulously | 90 |
| abstract_inverted_index.representing | 60 |
| abstract_inverted_index.substantiate | 161 |
| abstract_inverted_index.Comprehensive | 84 |
| abstract_inverted_index.architecture, | 15 |
| abstract_inverted_index.computational | 52 |
| abstract_inverted_index.painstakingly | 126 |
| abstract_inverted_index.Revision-based | 139 |
| abstract_inverted_index.learning-based | 27 |
| abstract_inverted_index.high-resolution | 106 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[1].score | 0.4300000071525574 |
| sustainable_development_goals[1].display_name | Affordable and clean energy |
| citation_normalized_percentile |