Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection
ABSTRACT Data augmentation plays an important role in boosting the performance of 3D models, while very few studies handle the 3D point cloud data with this technique. Global augmentation and cut‐paste are commonly used augmentation techniques for point clouds, where global augmentation is applied t...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.70001 |
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| author | Huaijin Liu Jixiang Du Yong Zhang Hongbo Zhang Jiandian Zeng |
| author_facet | Huaijin Liu Jixiang Du Yong Zhang Hongbo Zhang Jiandian Zeng |
| author_sort | Huaijin Liu |
| collection | DOAJ |
| description | ABSTRACT Data augmentation plays an important role in boosting the performance of 3D models, while very few studies handle the 3D point cloud data with this technique. Global augmentation and cut‐paste are commonly used augmentation techniques for point clouds, where global augmentation is applied to the entire point cloud of the scene, and cut‐paste samples objects from other frames into the current frame. Both types of data augmentation can improve performance, but the cut‐paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling, which may be counterproductive and may hurt the overall performance. In addition, LiDAR is susceptible to signal loss, external occlusion, extreme weather and other factors, which can easily cause object shape changes, while global augmentation and cut‐paste cannot effectively enhance the robustness of the model. To this end, we propose Syn‐Aug, a synchronous data augmentation framework for LiDAR‐based 3D object detection. Specifically, we first propose a novel rendering‐based object augmentation technique (Ren‐Aug) to enrich training data while enhancing scene realism. Second, we propose a local augmentation technique (Local‐Aug) to generate local noise by rotating and scaling objects in the scene while avoiding collisions, which can improve generalisation performance. Finally, we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames. We verify the proposed framework with four different types of 3D object detectors. Experimental results show that our proposed Syn‐Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets, proving the effectiveness and generality of Syn‐Aug. On KITTI, four different types of baseline models using Syn‐Aug improved mAP by 0.89%, 1.35%, 1.61% and 1.14% respectively. On nuScenes, four different types of baseline models using Syn‐Aug improved mAP by 14.93%, 10.42%, 8.47% and 6.81% respectively. The code is available at https://github.com/liuhuaijjin/Syn‐Aug. |
| format | Article |
| id | doaj-art-52661a99acee46ff92a60d751facfe8a |
| institution | Kabale University |
| issn | 2468-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-52661a99acee46ff92a60d751facfe8a2025-08-20T03:24:17ZengWileyCAAI Transactions on Intelligence Technology2468-23222025-06-0110391292810.1049/cit2.70001Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object DetectionHuaijin Liu0Jixiang Du1Yong Zhang2Hongbo Zhang3Jiandian Zeng4School of Artificial Intelligence Guangzhou Maritime University China and College of Mechanical Engineering and Automation Huaqiao University Xiamen ChinaCollege of Computer Science and Technology Huaqiao University Xiamen ChinaCollege of Mechanical Engineering and Automation Huaqiao University Xiamen ChinaCollege of Computer Science and Technology Huaqiao University Xiamen ChinaInstitute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai ChinaABSTRACT Data augmentation plays an important role in boosting the performance of 3D models, while very few studies handle the 3D point cloud data with this technique. Global augmentation and cut‐paste are commonly used augmentation techniques for point clouds, where global augmentation is applied to the entire point cloud of the scene, and cut‐paste samples objects from other frames into the current frame. Both types of data augmentation can improve performance, but the cut‐paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling, which may be counterproductive and may hurt the overall performance. In addition, LiDAR is susceptible to signal loss, external occlusion, extreme weather and other factors, which can easily cause object shape changes, while global augmentation and cut‐paste cannot effectively enhance the robustness of the model. To this end, we propose Syn‐Aug, a synchronous data augmentation framework for LiDAR‐based 3D object detection. Specifically, we first propose a novel rendering‐based object augmentation technique (Ren‐Aug) to enrich training data while enhancing scene realism. Second, we propose a local augmentation technique (Local‐Aug) to generate local noise by rotating and scaling objects in the scene while avoiding collisions, which can improve generalisation performance. Finally, we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames. We verify the proposed framework with four different types of 3D object detectors. Experimental results show that our proposed Syn‐Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets, proving the effectiveness and generality of Syn‐Aug. On KITTI, four different types of baseline models using Syn‐Aug improved mAP by 0.89%, 1.35%, 1.61% and 1.14% respectively. On nuScenes, four different types of baseline models using Syn‐Aug improved mAP by 14.93%, 10.42%, 8.47% and 6.81% respectively. The code is available at https://github.com/liuhuaijjin/Syn‐Aug.https://doi.org/10.1049/cit2.700013D object detectiondata augmentationdiversitygeneralizationpoint cloudrobustness |
| spellingShingle | Huaijin Liu Jixiang Du Yong Zhang Hongbo Zhang Jiandian Zeng Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection CAAI Transactions on Intelligence Technology 3D object detection data augmentation diversity generalization point cloud robustness |
| title | Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection |
| title_full | Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection |
| title_fullStr | Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection |
| title_full_unstemmed | Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection |
| title_short | Syn‐Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection |
| title_sort | syn aug an effective and general synchronous data augmentation framework for 3d object detection |
| topic | 3D object detection data augmentation diversity generalization point cloud robustness |
| url | https://doi.org/10.1049/cit2.70001 |
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