SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion
arXiv:2606.27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benchmarks often emphasize image-level recognition, single-view understanding, or narrow answer formats, leaving 3D spatial inference, multi-view collaboration, scene dynamics, and diverse task formulations insufficiently evaluated. To address these gaps, we introduce SpatialUAV, a real low-altitude UAV benchmark comprising 4,331 curated instances across 14 fine-grained task types, covering semantic d
The development of a new comprehensive benchmark addresses critical gaps in evaluating spatial intelligence for low-altitude UAVs, driven by the increasing complexity and autonomy demands for drone applications.
This benchmark is crucial for advancing UAV capabilities beyond basic image recognition, enabling more sophisticated autonomous operations in complex 3D environments, impacting defense, logistics, and surveillance.
The focus shifts from image-level understanding to robust 3D spatial inference and multi-view collaboration, fostering more capable and situationally aware autonomous UAV systems.
- · Defence contractors
- · UAV manufacturers
- · AI software developers
- · Logistics companies
- · Companies with limited 3D perception AI capabilities
Improved spatial intelligence will lead to more reliable and autonomous low-altitude UAV operations.
Enhanced UAV autonomy will reduce the need for constant human oversight, enabling larger-scale deployments and new applications.
The widespread adoption of highly autonomous UAVs, particularly in defense, could redefine surveillance, reconnaissance, and combat strategies.
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Read at arXiv cs.AI