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

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

doi:10.1109/JSTARS.2020.3032221

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

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

Whiting School of Engineering

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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.

Original language	English (US)
Article number	9229514
Pages (from-to)	922-935
Number of pages	14
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	14
DOIs	https://doi.org/10.1109/JSTARS.2020.3032221
State	Published - 2021

Keywords

3-D reconstruction
Classification
Data Fusion Contest (DFC)
convolutional neural network (CNN)
deep learning
elevation model
height estimation
image analysis and data fusion (IADF)
light detection and ranging (LiDAR)
multiview
point cloud
semantic labeling
semantic mapping
stereo

ASJC Scopus subject areas

Computers in Earth Sciences
Atmospheric Science

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10.1109/JSTARS.2020.3032221

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Kunwar, S., Chen, H., Lin, M., Zhang, H., D'Angelo, P., Cerra, D., Azimi, S. M., Brown, M., Hager, G., Yokoya, N., Hansch, R., & Le Saux, B. (2021). 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, 14, 922-935. Article 9229514. https://doi.org/10.1109/JSTARS.2020.3032221

Kunwar, S, Chen, H, Lin, M, Zhang, H, D'Angelo, P, Cerra, D, Azimi, SM, Brown, M, Hager, G, Yokoya, N, Hansch, R & Le Saux, B 2021, '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, vol. 14, 9229514, pp. 922-935. https://doi.org/10.1109/JSTARS.2020.3032221

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

abstract = "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.",

keywords = "3-D reconstruction, Classification, Data Fusion Contest (DFC), convolutional neural network (CNN), deep learning, elevation model, height estimation, image analysis and data fusion (IADF), light detection and ranging (LiDAR), multiview, point cloud, semantic labeling, semantic mapping, stereo",

author = "Saket Kunwar and Hongyu Chen and Manhui Lin and Hongyan Zhang and Pablo D'Angelo and Daniele Cerra and Azimi, {Seyed Majid} and Myron Brown and Gregory Hager and Naoto Yokoya and Ronny Hansch and {Le Saux}, Bertrand",

note = "Funding Information: Manuscript received July 31, 2020; revised October 5, 2020; accepted October 8, 2020. Date of publication October 19, 2020; date of current version January 6, 2021. The work of Myron Brown was supported by the Intelligence Advanced Research Projects Activity under Contract 2017-17032700004. (Corresponding author: Ronny H{\"a}nsch.) Saket Kunwar is with NestAI, Kathmandu 44600, Nepal (e-mail: saketkunwar2005@gmail.com). Publisher Copyright: {\textcopyright} 2008-2012 IEEE.",

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doi = "10.1109/JSTARS.2020.3032221",

language = "English (US)",

volume = "14",

pages = "922--935",

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AU - Zhang, Hongyan

AU - D'Angelo, Pablo

AU - Cerra, Daniele

AU - Azimi, Seyed Majid

AU - Brown, Myron

AU - Hager, Gregory

AU - Yokoya, Naoto

AU - Hansch, Ronny

AU - Le Saux, Bertrand

N1 - Funding Information: Manuscript received July 31, 2020; revised October 5, 2020; accepted October 8, 2020. Date of publication October 19, 2020; date of current version January 6, 2021. The work of Myron Brown was supported by the Intelligence Advanced Research Projects Activity under Contract 2017-17032700004. (Corresponding author: Ronny Hänsch.) Saket Kunwar is with NestAI, Kathmandu 44600, Nepal (e-mail: saketkunwar2005@gmail.com). Publisher Copyright: © 2008-2012 IEEE.

PY - 2021

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N2 - 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.

AB - 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.

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KW - elevation model

KW - height estimation

KW - image analysis and data fusion (IADF)

KW - light detection and ranging (LiDAR)

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KW - point cloud

KW - semantic labeling

KW - semantic mapping

KW - stereo

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

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Keywords

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