A weakly supervised method for 3D object detection with partially annotated samples
In numerous practical applications, particularly in the field of autonomous driving, acquiring annotated datasets that include both images and LiDAR point clouds simultaneously presents significant challenges and incurs substantial costs. To overcome the limitations of limited sample annotations, we...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940241297568 |
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| _version_ | 1850093910222700544 |
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| author | Bin Lu Qing Li Yanju Liang |
| author_facet | Bin Lu Qing Li Yanju Liang |
| author_sort | Bin Lu |
| collection | DOAJ |
| description | In numerous practical applications, particularly in the field of autonomous driving, acquiring annotated datasets that include both images and LiDAR point clouds simultaneously presents significant challenges and incurs substantial costs. To overcome the limitations of limited sample annotations, we propose an innovative weakly supervised learning methodology that utilizes reciprocal knowledge transfer between image detection models and 3D point cloud detection models. To the best of our knowledge, this area has not been explored by prior research teams. Our approach effectively addresses the alignment challenge of diverse modal features from an aerial perspective. Through heatmap prediction, we successfully facilitate knowledge transfer between the image detection and 3D point cloud detection models. Additionally, we conduct extensive experiments to evaluate the performance of our models under different parameters in the domain adaptation process, employing Exponential Moving Average (EMA) progressive learning. Furthermore, we explore the advantages of incorporating regression and prediction fusion heads to enhance weakly supervised learning. Remarkably, our experimental results on the widely accessible KITTI datasets demonstrate that our proposed approach achieves outstanding performance in 3D object detection under weak supervision, surpassing the baseline performance of the original 3D point cloud detection model. |
| format | Article |
| id | doaj-art-3fdee36b159448b9ab27e70f4adb74c8 |
| institution | DOAJ |
| issn | 0020-2940 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj-art-3fdee36b159448b9ab27e70f4adb74c82025-08-20T02:41:48ZengSAGE PublishingMeasurement + Control0020-29402025-04-015810.1177/00202940241297568A weakly supervised method for 3D object detection with partially annotated samplesBin Lu0Qing Li1Yanju Liang2Wuxi IoT Innovation Center Co., Wuxi, ChinaInstitute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029, ChinaWuxi IoT Innovation Center Co., Wuxi, ChinaIn numerous practical applications, particularly in the field of autonomous driving, acquiring annotated datasets that include both images and LiDAR point clouds simultaneously presents significant challenges and incurs substantial costs. To overcome the limitations of limited sample annotations, we propose an innovative weakly supervised learning methodology that utilizes reciprocal knowledge transfer between image detection models and 3D point cloud detection models. To the best of our knowledge, this area has not been explored by prior research teams. Our approach effectively addresses the alignment challenge of diverse modal features from an aerial perspective. Through heatmap prediction, we successfully facilitate knowledge transfer between the image detection and 3D point cloud detection models. Additionally, we conduct extensive experiments to evaluate the performance of our models under different parameters in the domain adaptation process, employing Exponential Moving Average (EMA) progressive learning. Furthermore, we explore the advantages of incorporating regression and prediction fusion heads to enhance weakly supervised learning. Remarkably, our experimental results on the widely accessible KITTI datasets demonstrate that our proposed approach achieves outstanding performance in 3D object detection under weak supervision, surpassing the baseline performance of the original 3D point cloud detection model.https://doi.org/10.1177/00202940241297568 |
| spellingShingle | Bin Lu Qing Li Yanju Liang A weakly supervised method for 3D object detection with partially annotated samples Measurement + Control |
| title | A weakly supervised method for 3D object detection with partially annotated samples |
| title_full | A weakly supervised method for 3D object detection with partially annotated samples |
| title_fullStr | A weakly supervised method for 3D object detection with partially annotated samples |
| title_full_unstemmed | A weakly supervised method for 3D object detection with partially annotated samples |
| title_short | A weakly supervised method for 3D object detection with partially annotated samples |
| title_sort | weakly supervised method for 3d object detection with partially annotated samples |
| url | https://doi.org/10.1177/00202940241297568 |
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