UCDNet: Multi-UAV Collaborative 3-D Object Detection Network by Reliable Feature Mapping

Multi-unmanned aerial vehicle (UAV) collaborative 3-D object detection can comprehend complex environments by integrating complementary information, with applications encompassing traffic monitoring, delivery services, and agricultural management. However, the extremely broad observations in aerial remote sensing and significant perspective differences across multiple UAVs make it challenging to achieve precise and consistent feature mapping from 2-D images to 3-D space in multi-UAV collaborative 3-D object detection paradigm. To address the problem, we propose an unparalleled camera-based multi-UAV collaborative 3-D object detection paradigm called UCDNet. Specifically, the depth information from the UAVs to the ground is explicitly utilized as a strong prior to provide a reference for more accurate and generalizable feature mapping. Additionally, we design a homologous point geometric consistency loss as an auxiliary self-supervision, which directly influences the feature mapping module, thereby strengthening the global consistency of multiview perception. Experiments on AeroCollab3D and CoPerception-UAVs datasets show that our method increases 4.7% and 10% mean Average Precision (mAP) respectively compared to the baseline, which demonstrates the superiority of UCDNet.