SLPOD: superclass learning on point cloud object detection
Abstract In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional mode...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01781-4 |
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| author | Xiaokang Yang Kai Zhang Yangyue Feng Beibei Su Yiming Cai Kaibo Zhang Zhiheng Zhang |
| author_facet | Xiaokang Yang Kai Zhang Yangyue Feng Beibei Su Yiming Cai Kaibo Zhang Zhiheng Zhang |
| author_sort | Xiaokang Yang |
| collection | DOAJ |
| description | Abstract In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional models struggle to adequately capture individual-specific features, resulting in a scattered distribution of samples within the feature space and compromising the precision of object bounding boxes. To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. Additionally, our approach integrates strategies such as voxel and point cloud feature fusion, global feature acquisition, and dynamic adjustment of sampling rates based on point sparsity, further enhancing the network’s capability to extract features. Experimental results demonstrate that SLPOD outperforms baseline algorithms in mean Average Precision on both KITTI and Waymo datasets, exhibiting robustness across diverse scenarios. |
| format | Article |
| id | doaj-art-a815f6bc06064cf7b6e2367ea19374f2 |
| institution | DOAJ |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-a815f6bc06064cf7b6e2367ea19374f22025-08-20T02:55:32ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-03-0111511510.1007/s40747-025-01781-4SLPOD: superclass learning on point cloud object detectionXiaokang Yang0Kai Zhang1Yangyue Feng2Beibei Su3Yiming Cai4Kaibo Zhang5Zhiheng Zhang6School of Information Engineering, Changchun University of Finance and EconomicsSchool of Information Engineering, Changchun University of Finance and EconomicsSchool of Computer Science and Engineering, Changchun University of TechnologySchool of Computer Science and Engineering, Changchun University of TechnologySchool of Information Engineering, Changchun University of Finance and EconomicsSchool of Information Engineering, Changchun University of Finance and EconomicsSchool of Information Engineering, Changchun University of Finance and EconomicsAbstract In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional models struggle to adequately capture individual-specific features, resulting in a scattered distribution of samples within the feature space and compromising the precision of object bounding boxes. To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. Additionally, our approach integrates strategies such as voxel and point cloud feature fusion, global feature acquisition, and dynamic adjustment of sampling rates based on point sparsity, further enhancing the network’s capability to extract features. Experimental results demonstrate that SLPOD outperforms baseline algorithms in mean Average Precision on both KITTI and Waymo datasets, exhibiting robustness across diverse scenarios.https://doi.org/10.1007/s40747-025-01781-4Point cloud object detectionContrastive learningUnsupervised learningDeep learning |
| spellingShingle | Xiaokang Yang Kai Zhang Yangyue Feng Beibei Su Yiming Cai Kaibo Zhang Zhiheng Zhang SLPOD: superclass learning on point cloud object detection Complex & Intelligent Systems Point cloud object detection Contrastive learning Unsupervised learning Deep learning |
| title | SLPOD: superclass learning on point cloud object detection |
| title_full | SLPOD: superclass learning on point cloud object detection |
| title_fullStr | SLPOD: superclass learning on point cloud object detection |
| title_full_unstemmed | SLPOD: superclass learning on point cloud object detection |
| title_short | SLPOD: superclass learning on point cloud object detection |
| title_sort | slpod superclass learning on point cloud object detection |
| topic | Point cloud object detection Contrastive learning Unsupervised learning Deep learning |
| url | https://doi.org/10.1007/s40747-025-01781-4 |
| work_keys_str_mv | AT xiaokangyang slpodsuperclasslearningonpointcloudobjectdetection AT kaizhang slpodsuperclasslearningonpointcloudobjectdetection AT yangyuefeng slpodsuperclasslearningonpointcloudobjectdetection AT beibeisu slpodsuperclasslearningonpointcloudobjectdetection AT yimingcai slpodsuperclasslearningonpointcloudobjectdetection AT kaibozhang slpodsuperclasslearningonpointcloudobjectdetection AT zhihengzhang slpodsuperclasslearningonpointcloudobjectdetection |