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Large-Scale Semantic 3D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A

Kunwar, Saket and Chen, Hongyu and Lin, Manhui and Zhang, Hongyan and d'Angelo, Pablo and Cerra, Daniele and Azimi, Seyed Majid and Brown, Myron and Hager, Gregory and Yokoya, Naoto and Hänsch, Ronny and Le Saux, Bertrand (2020) Large-Scale Semantic 3D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp. 922-935. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.3032221. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/abstract/document/9229514

Abstract

In this paper, 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 3D reconstruction and 3D 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 this Part A, we report the results of the best-performing approaches for semantic 3D reconstruction according to these various set-ups, while 3D point cloud semantic mapping is discussed in Part B.

Item URL in elib:https://elib.dlr.de/138023/
Document Type:Article
Title:Large-Scale Semantic 3D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Kunwar, SaketNestAI, NestAI, Kathmandu NepalUNSPECIFIED
Chen, Hongyuhongyuchen (at) whu.edu.cnUNSPECIFIED
Lin, Manhuimhlin425 (at) whu.edu.cnUNSPECIFIED
Zhang, Hongyanzhanghongyan (at) whu.edu.cnUNSPECIFIED
d'Angelo, Pablopablo.angelo (at) dlr.dehttps://orcid.org/0000-0001-8541-3856
Cerra, Danieledaniele.cerra (at) dlr.dehttps://orcid.org/0000-0003-2984-8315
Azimi, Seyed Majidseyedmajid.azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Brown, Myronmyron.brown (at) jhuapl.eduUNSPECIFIED
Hager, Gregoryhager (at) cs.jhu.eduUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Hänsch, RonnyRonny.Haensch (at) dlr.dehttps://orcid.org/0000-0002-2936-6765
Le Saux, Bertrandbertrand.le_saux (at) onera.frUNSPECIFIED
Date:19 October 2020
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.1109/JSTARS.2020.3032221
Page Range:pp. 922-935
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Image analysis and data fusion, data fusion contest, stereo, multi-view, 3D reconstruction, height estimation, elevation model, point-cloud, semantic labeling, semantic mapping, classification, LiDAR, deep learning, convolutional neural networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Microwaves and Radar Institute > SAR Technology
Deposited By: Cerra, Daniele
Deposited On:25 Nov 2020 12:46
Last Modified:14 Jan 2021 17:36

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