CenterPoint-UAV: Context-Detail BEV Refinement for 3D Object Detection in UAV Point Clouds.
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| Title: | CenterPoint-UAV: Context-Detail BEV Refinement for 3D Object Detection in UAV Point Clouds. |
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| Authors: | Wu, Yutian1,2 (AUTHOR), Mei, Sifan1,2 (AUTHOR), Liu, Shengli3 (AUTHOR), Wang, Yichen4 (AUTHOR), Ogai, Harutoshi1,4 (AUTHOR), Li, Qing1,2 (AUTHOR) liqing@ies.ustb.edu.cn |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 17, p2994. 26p. |
| Subjects: | Point cloud, Remote sensing, Object recognition (Computer vision) |
| Abstract: | UAV-based 3D object detection is important for remote-sensing applications such as autonomous inspection, search and rescue, aerial mapping, flight cooperation, and scene-level environmental understanding, where a detector must localize diverse objects in large and sparsely observed point clouds. Existing 3D object detectors provide a strong foundation, but most of them are developed around autonomous-driving scenarios and are not fully adapted to UAV scenes. Compared with road scenes, UAV point clouds usually cover larger areas, contain more diverse object categories, and include many small, sparse, and structurally varied targets. Many small targets therefore occupy only a few BEV cells and contain limited point returns; subsequent feature aggregation and downsampling can further smooth these sparse local responses, making object boundaries and center-related responses less distinguishable from the background. We propose CenterPoint-UAV, an end-to-end voxel-based detector that refines BEV features for UAV-based 3D object detection. CenterPoint-UAV introduces Context-Detail BEV Enhance (CDBE), which uses a Context Enhancement Branch (CEB) and a Detail Enhancement Branch (DEB) to produce complementary BEV feature maps and fuses them using Adaptive Residual Fusion (ARF). It then uses Cross-Level BEV Fusion (CLBF) to combine early BEV details with deep semantic features, followed by a Fine Center Head (FCH) for denser center prediction. Experiments on WiSAR3D, a large-scale real-world UAV point-cloud dataset for multi-category object detection, show that CenterPoint-UAV achieves state-of-the-art mAP among existing methods and maintains a low parameter budget, demonstrating its effectiveness for UAV-based 3D remote sensing. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | UAV-based 3D object detection is important for remote-sensing applications such as autonomous inspection, search and rescue, aerial mapping, flight cooperation, and scene-level environmental understanding, where a detector must localize diverse objects in large and sparsely observed point clouds. Existing 3D object detectors provide a strong foundation, but most of them are developed around autonomous-driving scenarios and are not fully adapted to UAV scenes. Compared with road scenes, UAV point clouds usually cover larger areas, contain more diverse object categories, and include many small, sparse, and structurally varied targets. Many small targets therefore occupy only a few BEV cells and contain limited point returns; subsequent feature aggregation and downsampling can further smooth these sparse local responses, making object boundaries and center-related responses less distinguishable from the background. We propose CenterPoint-UAV, an end-to-end voxel-based detector that refines BEV features for UAV-based 3D object detection. CenterPoint-UAV introduces Context-Detail BEV Enhance (CDBE), which uses a Context Enhancement Branch (CEB) and a Detail Enhancement Branch (DEB) to produce complementary BEV feature maps and fuses them using Adaptive Residual Fusion (ARF). It then uses Cross-Level BEV Fusion (CLBF) to combine early BEV details with deep semantic features, followed by a Fine Center Head (FCH) for denser center prediction. Experiments on WiSAR3D, a large-scale real-world UAV point-cloud dataset for multi-category object detection, show that CenterPoint-UAV achieves state-of-the-art mAP among existing methods and maintains a low parameter budget, demonstrating its effectiveness for UAV-based 3D remote sensing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18172994 |
