3D Object Detection for Autonomous Driving: A Survey
•Notice that no recent literature exists to collect the growing knowledge concerning 3D object detection, we fill this gap by starting with several basic concepts, providing a glimpse of evolution of 3D object detection, together with comprehensive comparisons on publicly available datasets being ma...
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| Veröffentlicht in: | Pattern recognition Jg. 130; S. 108796 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.10.2022
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | •Notice that no recent literature exists to collect the growing knowledge concerning 3D object detection, we fill this gap by starting with several basic concepts, providing a glimpse of evolution of 3D object detection, together with comprehensive comparisons on publicly available datasets being manifested, with pros and cons being judiciously presented.•Witnessing the absence of a universal consensus on taxonomy with respect to 3D object detection, we contribute to the maturity of the taxonomy, which keeps a good continuity of existing efforts as well as adapts new branches for dynamics.•We present a case study on fifteen selected models among surveyed works, with regard to runtime analysis, error analysis, and robustness analysis closely. We argue that what mainly restricts the performance of detection is 3D location error based on our findings.
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc.. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work. |
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| AbstractList | •Notice that no recent literature exists to collect the growing knowledge concerning 3D object detection, we fill this gap by starting with several basic concepts, providing a glimpse of evolution of 3D object detection, together with comprehensive comparisons on publicly available datasets being manifested, with pros and cons being judiciously presented.•Witnessing the absence of a universal consensus on taxonomy with respect to 3D object detection, we contribute to the maturity of the taxonomy, which keeps a good continuity of existing efforts as well as adapts new branches for dynamics.•We present a case study on fifteen selected models among surveyed works, with regard to runtime analysis, error analysis, and robustness analysis closely. We argue that what mainly restricts the performance of detection is 3D location error based on our findings.
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc.. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work. |
| ArticleNumber | 108796 |
| Author | Li, Xirong Qian, Rui Lai, Xin |
| Author_xml | – sequence: 1 givenname: Rui orcidid: 0000-0003-0753-7049 surname: Qian fullname: Qian, Rui email: rui-qian@ruc.edu.cn organization: Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing 100872, China – sequence: 2 givenname: Xin orcidid: 0000-0003-0758-5219 surname: Lai fullname: Lai, Xin email: laixin@ruc.edu.cn organization: School of Mathematics, Renmin University of China, Beijing 100872, China – sequence: 3 givenname: Xirong surname: Li fullname: Li, Xirong email: xirong@ruc.edu.cn organization: Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing 100872, China |
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volume 29 Mao (10.1016/j.patcog.2022.108796_bib0005) 2021 Mai (10.1016/j.patcog.2022.108796_sbref0043) 2017 Sheng (10.1016/j.patcog.2022.108796_bib0029) 2021 Kuang (10.1016/j.patcog.2022.108796_bib0055) 2020; volume 20 Li (10.1016/j.patcog.2022.108796_bib0009) 2018 Weng (10.1016/j.patcog.2022.108796_bib0041) 2019 Ma (10.1016/j.patcog.2022.108796_bib0063) 2019 Everingham (10.1016/j.patcog.2022.108796_bib0067) 2010; 88 Feng (10.1016/j.patcog.2022.108796_bib0036) 2021; volume 22 Yin (10.1016/j.patcog.2022.108796_bib0023) 2021 Geiger (10.1016/j.patcog.2022.108796_bib0060) 2013; volume 32 Chang (10.1016/j.patcog.2022.108796_bib0002) 2018 Zhou (10.1016/j.patcog.2022.108796_bib0054) 2020 Chang (10.1016/j.patcog.2022.108796_bib0065) 2019 Yang (10.1016/j.patcog.2022.108796_bib0051) 2020 Liang (10.1016/j.patcog.2022.108796_bib0032) 2018; volume 11220 Lin (10.1016/j.patcog.2022.108796_bib0074) 2017 Tu (10.1016/j.patcog.2022.108796_bib0097) 2020 Chen (10.1016/j.patcog.2022.108796_bib0047) 2018; volume 40 Ren (10.1016/j.patcog.2022.108796_bib0071) 2015 Song (10.1016/j.patcog.2022.108796_bib0042) 2016 Chen (10.1016/j.patcog.2022.108796_bib0066) 2015 Yan (10.1016/j.patcog.2022.108796_bib0025) 2018; volume 18 Park (10.1016/j.patcog.2022.108796_bib0020) 2021 Zheng (10.1016/j.patcog.2022.108796_bib0026) 2021 Qian (10.1016/j.patcog.2022.108796_bib0088) 2022; volume 125 Yoo (10.1016/j.patcog.2022.108796_bib0033) 2020 Qi (10.1016/j.patcog.2022.108796_bib0082) 2017 Roddick (10.1016/j.patcog.2022.108796_bib0081) 2019 Li (10.1016/j.patcog.2022.108796_bib0017) 2019 Halloran (10.1016/j.patcog.2022.108796_bib0058) 2020 Chabot (10.1016/j.patcog.2022.108796_bib0015) 2017 Qi (10.1016/j.patcog.2022.108796_bib0089) 2018 Guo (10.1016/j.patcog.2022.108796_bib0079) 2021 He (10.1016/j.patcog.2022.108796_bib0070) 2014; volume 8691 Wang (10.1016/j.patcog.2022.108796_bib0096) 2019 Li (10.1016/j.patcog.2022.108796_bib0018) 2019 Vaswani (10.1016/j.patcog.2022.108796_bib0094) 2017 Zheng (10.1016/j.patcog.2022.108796_bib0027) 2021 Chen (10.1016/j.patcog.2022.108796_bib0004) 2020 Shi (10.1016/j.patcog.2022.108796_bib0039) 2020 Yang (10.1016/j.patcog.2022.108796_bib0049) 2018 Kesten (10.1016/j.patcog.2022.108796_bib0061) 2019 Zhang (10.1016/j.patcog.2022.108796_bib0084) 2021 Chen (10.1016/j.patcog.2022.108796_bib0044) 2016 Reading (10.1016/j.patcog.2022.108796_bib0080) 2021 Lang (10.1016/j.patcog.2022.108796_bib0006) 2019 |
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