  Multi-UAV collaborative 3D object detection can perceive and 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 2D
images to 3D space in multi-UAV collaborative 3D object detection paradigm. To
address the problem, we propose an unparalleled camera-based multi-UAV
collaborative 3D 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 points geometric consistency loss
as an auxiliary self-supervision, which directly influences the feature mapping
module, thereby strengthening the global consistency of multi-view perception.
Experiments on AeroCollab3D and CoPerception-UAVs datasets show our method
increases 4.7% and 10% mAP respectively compared to the baseline, which
demonstrates the superiority of UCDNet.
