2D Image-Based 3D Scene Retrieval Article Swipe
YOU?
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· 2018
· Open Access
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· DOI: https://doi.org/10.2312/3dor.20181051
2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retrieval. Given a 2D scene image, it is to search for relevant 3D scenes from a dataset. It has an intuitive and convenient framework which allows users to learn, search, and utilize the retrieved results for vast related applications, such as automatic 3D content generation for 3D movie, game and animation production, robotic vision, and consumer electronics apps development, and autonomous vehicles. To advance this promising research, we organize this SHREC track and build the first 2D scene image-based 3D scene retrieval benchmark by collecting 2D images from ImageNet and 3D scenes from Google 3D Warehouse. The benchmark contains uniformly classified 10,000 2D scene images and 1,000 3D scene models of ten (10) categories. In this track, seven (7) groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only three (3) groups have successfully submitted ten (10) runs of five methods. To have a comprehensive comparison, seven (7) commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. We also suggest several future research directions for this research topic. We wish this publicly available [ARYLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and boost the research of 2D scene image-based 3D scene retrieval and its applications.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2312/3dor.20181051
- OA Status
- green
- Cited By
- 8
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2887582123
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2887582123Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2312/3dor.20181051Digital Object Identifier
- Title
-
2D Image-Based 3D Scene RetrievalWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Hameed Abdul-Rashid, Juefei Yuan, Bo Li, Yijuan Lu, Song Bai, Xiang Bai, Ngoc-Minh Bui, N. Minh, Trong-Le Do, Anh Duc Duong, Xinwei He, Tu-Khiem Le, Wenhui Li, An-An Liu, Xiaolong Liu, Khac-Tuan Nguyen, Vinh-Tiep Nguyen, Weizhi Nie, Van-Tu Ninh, Yuting Su, Vinh Ton-That, Minh–Triet Tran, Shu Xiang, Heyu Zhou, Yang Zhou, Zhichao ZhouList of authors in order
- Landing page
-
https://doi.org/10.2312/3dor.20181051Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2312/3dor.20181051Direct OA link when available
- Concepts
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Computer science, Computer vision, Artificial intelligence, Image retrieval, Visual Word, Image (mathematics), Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 1, 2020: 2, 2019: 3, 2018: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.generation | 61 |
| abstract_inverted_index.registered | 147 |
| abstract_inverted_index.retrieval. | 17 |
| abstract_inverted_index.categories. | 130 |
| abstract_inverted_index.comparative | 207 |
| abstract_inverted_index.comparison, | 174 |
| abstract_inverted_index.electronics | 73 |
| abstract_inverted_index.image-based | 2, 95, 224 |
| abstract_inverted_index.performance | 179 |
| abstract_inverted_index.production, | 68 |
| abstract_inverted_index.development, | 75 |
| abstract_inverted_index.performance. | 188 |
| abstract_inverted_index.successfully | 162 |
| abstract_inverted_index.applications, | 55 |
| abstract_inverted_index.applications. | 230 |
| abstract_inverted_index.commonly-used | 177 |
| abstract_inverted_index.comprehensive | 173 |
| abstract_inverted_index.corresponding | 211 |
| cited_by_percentile_year | |
| countries_distinct_count | 4 |
| institutions_distinct_count | 26 |
| citation_normalized_percentile |