SECOND: Sparsely Embedded Convolutional Detection
LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain,...
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| Published in | Sensors (Basel, Switzerland) Vol. 18; no. 10; p. 3337 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI
06.10.2018
MDPI AG |
| Subjects | |
| Online Access | Get full text |
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| Abstract | LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed. |
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| AbstractList | LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed. LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed.LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed. |
| Author | Mao, Yuxing Li, Bo Yan, Yan |
| AuthorAffiliation | 1 State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China; scrin@foxmail.com 2 TrunkTech Co., Ltd., No. 3, Danling street, ZhongGuan Town, HaiDian District, Beijing 100089, China; libo@trunk.tech |
| AuthorAffiliation_xml | – name: 2 TrunkTech Co., Ltd., No. 3, Danling street, ZhongGuan Town, HaiDian District, Beijing 100089, China; libo@trunk.tech – name: 1 State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China; scrin@foxmail.com |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0002-3314-1518 surname: Yan fullname: Yan, Yan – sequence: 2 givenname: Yuxing surname: Mao fullname: Mao, Yuxing – sequence: 3 givenname: Bo surname: Li fullname: Li, Bo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30301196$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TPAMI.2017.2706685 10.1109/CVPR.2017.597 10.1109/CVPR.2018.00961 10.1109/CVPR.2016.91 10.1109/ICRA.2018.8461232 10.1007/978-3-319-46448-0_2 10.1109/CVPR.2017.691 10.1109/ICCV.2017.322 10.1109/IROS.2018.8594049 10.1109/CVPR.2018.00472 10.1109/CVPRW.2019.00158 10.1109/CVPR.2018.00742 10.1109/CVPR.2016.236 10.1109/ICCV.2015.169 10.1109/CVPR.2018.00798 10.5244/C.29.150 10.1109/ICRA.2017.7989161 10.1109/ASAP.2017.7995254 10.1109/CVPR.2012.6248074 10.1109/CVPR.2016.94 10.1109/ICCV.2017.324 10.1109/IROS.2017.8205955 10.1109/CVPR.2018.00102 10.1109/CVPR.2014.81 |
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| Snippet | LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional... |
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| Title | SECOND: Sparsely Embedded Convolutional Detection |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/30301196 https://www.proquest.com/docview/2117822581 https://pubmed.ncbi.nlm.nih.gov/PMC6210968 https://doaj.org/article/53a182b0009f4a1696d217c4a0a01249 |
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