Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 32; no. 8; pp. 3412 - 3432 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 2162-2388 |
| DOI | 10.1109/TNNLS.2020.3015992 |
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| Abstract | Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches. |
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| AbstractList | Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches. Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches.Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches. |
| Author | Cao, Dongpu Li, Ying Liu, Fei Chapman, Michael A. Ma, Lingfei Zhong, Zilong Li, Jonathan |
| Author_xml | – sequence: 1 givenname: Ying orcidid: 0000-0003-0608-9619 surname: Li fullname: Li, Ying email: y2424li@uwaterloo.ca organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Lingfei orcidid: 0000-0001-8893-9693 surname: Ma fullname: Ma, Lingfei email: l53ma@uwaterloo.ca organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada – sequence: 3 givenname: Zilong orcidid: 0000-0003-0104-9116 surname: Zhong fullname: Zhong, Zilong email: z26zhong@uwaterloo.ca organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Fei surname: Liu fullname: Liu, Fei email: fledaliu@xilinx.com organization: Xilinx Technology Beijing, Ltd., Beijing, China – sequence: 5 givenname: Michael A. surname: Chapman fullname: Chapman, Michael A. email: mchapman@ryerson.ca organization: Department of Civil Engineering, Ryerson University, Toronto, ON, Canada – sequence: 6 givenname: Dongpu orcidid: 0000-0001-7929-4336 surname: Cao fullname: Cao, Dongpu email: dongpu.cao@uwaterloo.ca organization: Waterloo Cognitive Autonomous Driving Laboratory, University of Waterloo, Waterloo, ON, Canada – sequence: 7 givenname: Jonathan orcidid: 0000-0001-7899-0049 surname: Li fullname: Li, Jonathan email: junli@uwaterloo.ca organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada |
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| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
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| DOI | 10.1109/TNNLS.2020.3015992 |
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| SubjectTerms | Autonomous driving Autonomous vehicles Classification Computer vision Deep learning deep learning (DL) Laser radar LiDAR Machine learning object classification Object detection Object recognition point clouds Polls & surveys Semantic segmentation Semantics Solid modeling Task analysis Three dimensional models Three-dimensional displays |
| Title | Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review |
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