Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A
In this article, we present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2019 Contest addressed the problem of 3-D reconstruction and 3-D semantic understanding on a...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9229514/ |
| _version_ | 1831540647845167104 |
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| author | Saket Kunwar Hongyu Chen Manhui Lin Hongyan Zhang Pablo D'Angelo Daniele Cerra Seyed Majid Azimi Myron Brown Gregory Hager Naoto Yokoya Ronny Hansch Bertrand Le Saux |
| author_facet | Saket Kunwar Hongyu Chen Manhui Lin Hongyan Zhang Pablo D'Angelo Daniele Cerra Seyed Majid Azimi Myron Brown Gregory Hager Naoto Yokoya Ronny Hansch Bertrand Le Saux |
| author_sort | Saket Kunwar |
| collection | DOAJ |
| description | In this article, we present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2019 Contest addressed the problem of 3-D reconstruction and 3-D semantic understanding on a large scale. Several competitions were organized to assess specific issues, such as elevation estimation and semantic mapping from a single view, two views, or multiple views. In Part A, we report the results of the best-performing approaches for semantic 3-D reconstruction according to these various setups, whereas 3-D point cloud semantic mapping is discussed in Part B. |
| first_indexed | 2024-12-17T00:02:37Z |
| format | Article |
| id | doaj.art-2935c93976974e42b44459b15b005e5c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-17T00:02:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2935c93976974e42b44459b15b005e5c2022-12-21T22:11:02ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-011492293510.1109/JSTARS.2020.30322219229514Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part ASaket Kunwar0Hongyu Chen1Manhui Lin2Hongyan Zhang3https://orcid.org/0000-0002-7894-5755Pablo D'Angelo4https://orcid.org/0000-0001-8541-3856Daniele Cerra5https://orcid.org/0000-0003-2984-8315Seyed Majid Azimi6https://orcid.org/0000-0002-6084-2272Myron Brown7Gregory Hager8https://orcid.org/0000-0002-6662-9763Naoto Yokoya9https://orcid.org/0000-0002-7321-4590Ronny Hansch10https://orcid.org/0000-0002-2936-6765Bertrand Le Saux11https://orcid.org/0000-0001-7162-6746NestAI, Kathmandu, NepalState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaGerman Aerospace Center, Weßling, GermanyGerman Aerospace Center, Weßling, GermanyGerman Aerospace Center, Weßling, GermanyJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USAJohns Hopkins University, Baltimore, MD, USAGraduate School of Frontier Sciences, The University of Tokyo, Chiba, JapanGerman Aerospace Center, Weßling, GermanyΦ-Lab, ESA Centre for Earth Observation, Frascati, ItalyIn this article, we present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2019 Contest addressed the problem of 3-D reconstruction and 3-D semantic understanding on a large scale. Several competitions were organized to assess specific issues, such as elevation estimation and semantic mapping from a single view, two views, or multiple views. In Part A, we report the results of the best-performing approaches for semantic 3-D reconstruction according to these various setups, whereas 3-D point cloud semantic mapping is discussed in Part B.https://ieeexplore.ieee.org/document/9229514/Classificationconvolutional neural network (CNN)Data Fusion Contest (DFC)deep learningelevation modelheight estimation |
| spellingShingle | Saket Kunwar Hongyu Chen Manhui Lin Hongyan Zhang Pablo D'Angelo Daniele Cerra Seyed Majid Azimi Myron Brown Gregory Hager Naoto Yokoya Ronny Hansch Bertrand Le Saux Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification convolutional neural network (CNN) Data Fusion Contest (DFC) deep learning elevation model height estimation |
| title | Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A |
| title_full | Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A |
| title_fullStr | Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A |
| title_full_unstemmed | Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A |
| title_short | Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part A |
| title_sort | large scale semantic 3 d reconstruction outcome of the 2019 ieee grss data fusion contest x2014 part a |
| topic | Classification convolutional neural network (CNN) Data Fusion Contest (DFC) deep learning elevation model height estimation |
| url | https://ieeexplore.ieee.org/document/9229514/ |
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