Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network
The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms. Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost, low-power solution compared to LiDAR solutions in the field of autonomous driving. Howe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-08-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822000943 |
| _version_ | 1797727654680461312 |
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| author | Jianlong Zhang Guangzu Fang Bin Wang Xiaobo Zhou Qingqi Pei Chen Chen |
| author_facet | Jianlong Zhang Guangzu Fang Bin Wang Xiaobo Zhou Qingqi Pei Chen Chen |
| author_sort | Jianlong Zhang |
| collection | DOAJ |
| description | The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms. Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost, low-power solution compared to LiDAR solutions in the field of autonomous driving. However, this technique has some problems, i.e., (1) the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and (2) the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks. Therefore, we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection. We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence. Then, we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud. Finally, our detection framework is based on Point-Voxel-RCNN (PV-RCNN) with high-quality Pseudo-LiDAR and enriched geometric features as input. From the experimental results, our method achieves satisfactory results in monocular 3D object detection. |
| first_indexed | 2024-03-12T11:02:40Z |
| format | Article |
| id | doaj.art-9481d704abea4736807446a94ddb1f98 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-12T11:02:40Z |
| publishDate | 2023-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-9481d704abea4736807446a94ddb1f982023-09-02T04:31:47ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-08-0194827835Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G networkJianlong Zhang0Guangzu Fang1Bin Wang2Xiaobo Zhou3Qingqi Pei4Chen Chen5School of Electronic Engineering, Xidian University, Xi'an, 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi'an, 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi'an, 710071, China; Corresponding author.School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, 300000, ChinaSchool of Telecommunications Engineering, Xidian University, Xi'an, 710071, ChinaSchool of Telecommunications Engineering, Xidian University, Xi'an, 710071, China; Corresponding author.The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms. Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost, low-power solution compared to LiDAR solutions in the field of autonomous driving. However, this technique has some problems, i.e., (1) the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and (2) the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks. Therefore, we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection. We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence. Then, we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud. Finally, our detection framework is based on Point-Voxel-RCNN (PV-RCNN) with high-quality Pseudo-LiDAR and enriched geometric features as input. From the experimental results, our method achieves satisfactory results in monocular 3D object detection.http://www.sciencedirect.com/science/article/pii/S2352864822000943Monocular 3D object detectionPseudo-LiDARConfidence samplingHierarchical geometric feature extraction |
| spellingShingle | Jianlong Zhang Guangzu Fang Bin Wang Xiaobo Zhou Qingqi Pei Chen Chen Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network Digital Communications and Networks Monocular 3D object detection Pseudo-LiDAR Confidence sampling Hierarchical geometric feature extraction |
| title | Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network |
| title_full | Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network |
| title_fullStr | Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network |
| title_full_unstemmed | Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network |
| title_short | Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network |
| title_sort | monocular 3d object detection with pseudo lidar confidence sampling and hierarchical geometric feature extraction in 6g network |
| topic | Monocular 3D object detection Pseudo-LiDAR Confidence sampling Hierarchical geometric feature extraction |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822000943 |
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