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Deep Projective 3D Semantic Segmentation
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
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2017 (English)In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, p. 95-107Conference paper, Published paper (Refereed)
Abstract [en]

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets.

Place, publisher, year, edition, pages
Springer, 2017. p. 95-107
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keyword [en]
Point clouds, Semantic segmentation, Deep learning, Multi-stream deep networks
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-145374DOI: 10.1007/978-3-319-64689-3_8Scopus ID: 2-s2.0-85028506569ISBN: 9783319646886 (print)ISBN: 9783319646893 (electronic)OAI: oai:DiVA.org:liu-145374DiVA, id: diva2:1185653
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-03-02Bibliographically approved

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Järemo-Lawin, FelixDanelljan, MartinBhat, GoutamKhan, Fahad ShahbazFelsberg, Michael

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Järemo-Lawin, FelixDanelljan, MartinTosteberg, PatrikBhat, GoutamKhan, Fahad ShahbazFelsberg, Michael
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Computer Vision and Robotics (Autonomous Systems)Computer Engineering

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