SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds
Three-dimensional object detection using LiDAR has attracted significant attention due to its resilience to lighting conditions and ability to capture detailed geometric information. However, existing methods still face challenges, such as a high proportion of background points in the sampled point...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1064 |
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| author | Hengxin Xu Lei Yang Shengya Zhao Shan Tao Xinran Tian Kun Liu |
| author_facet | Hengxin Xu Lei Yang Shengya Zhao Shan Tao Xinran Tian Kun Liu |
| author_sort | Hengxin Xu |
| collection | DOAJ |
| description | Three-dimensional object detection using LiDAR has attracted significant attention due to its resilience to lighting conditions and ability to capture detailed geometric information. However, existing methods still face challenges, such as a high proportion of background points in the sampled point set and limited accuracy in detecting distant objects. To address these issues, we propose semantic-guided proposal sampling-RCNN (SPS-RCNN), a multi-stage detection framework based on point–voxel fusion. The framework comprises three components: a voxel-based region proposal network (RPN), a keypoint sampling stream (KSS), and a progressive refinement network (PRN). In the KSS, we propose a novel semantic-guided proposal sampling (SPS) method, which increases the proportion of foreground points and enhances sensitivity to outliers through multilevel sampling that integrates proposal-based local sampling and semantic-guided global sampling. In the PRN, a cascade attention module (CAM) is employed to aggregate features from multiple subnets, progressively refining region proposals to improve detection accuracy for medium- and long-range objects. Comprehensive experiments on the widely used KITTI dataset demonstrate that SPS-RCNN improves detection accuracy and exhibits enhanced robustness across categories compared to the baseline. |
| format | Article |
| id | doaj-art-6284c41e7082462a9262113fdcbd5e41 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6284c41e7082462a9262113fdcbd5e412025-08-20T02:44:50ZengMDPI AGSensors1424-82202025-02-01254106410.3390/s25041064SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point CloudsHengxin Xu0Lei Yang1Shengya Zhao2Shan Tao3Xinran Tian4Kun Liu5College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaNational Deep Sea Center, Qingdao 266237, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaNational Deep Sea Center, Qingdao 266237, ChinaThree-dimensional object detection using LiDAR has attracted significant attention due to its resilience to lighting conditions and ability to capture detailed geometric information. However, existing methods still face challenges, such as a high proportion of background points in the sampled point set and limited accuracy in detecting distant objects. To address these issues, we propose semantic-guided proposal sampling-RCNN (SPS-RCNN), a multi-stage detection framework based on point–voxel fusion. The framework comprises three components: a voxel-based region proposal network (RPN), a keypoint sampling stream (KSS), and a progressive refinement network (PRN). In the KSS, we propose a novel semantic-guided proposal sampling (SPS) method, which increases the proportion of foreground points and enhances sensitivity to outliers through multilevel sampling that integrates proposal-based local sampling and semantic-guided global sampling. In the PRN, a cascade attention module (CAM) is employed to aggregate features from multiple subnets, progressively refining region proposals to improve detection accuracy for medium- and long-range objects. Comprehensive experiments on the widely used KITTI dataset demonstrate that SPS-RCNN improves detection accuracy and exhibits enhanced robustness across categories compared to the baseline.https://www.mdpi.com/1424-8220/25/4/10643D object detectionlight detection and ranging (lidar)point–voxel fusionsemantic-guided proposal samplingcascade network |
| spellingShingle | Hengxin Xu Lei Yang Shengya Zhao Shan Tao Xinran Tian Kun Liu SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds Sensors 3D object detection light detection and ranging (lidar) point–voxel fusion semantic-guided proposal sampling cascade network |
| title | SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds |
| title_full | SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds |
| title_fullStr | SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds |
| title_full_unstemmed | SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds |
| title_short | SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds |
| title_sort | sps rcnn semantic guided proposal sampling for 3d object detection from lidar point clouds |
| topic | 3D object detection light detection and ranging (lidar) point–voxel fusion semantic-guided proposal sampling cascade network |
| url | https://www.mdpi.com/1424-8220/25/4/1064 |
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