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Depth completion and object detection are two crucial tasks often used foraerial 3D mapping, path planning, and collision avoidance of Uncrewed AerialVehicles (UAVs). Common solutions include using measurements from a LiDARsensor; however, the generated point cloud is often sparse and irregular andlimits the system's capabilities in 3D rendering and safety-criticaldecision-making. To mitigate this challenge, information from other sensors onthe UAV (viz., a camera used for object detection) is utilized to help thedepth completion process generate denser 3D models. Performing both aerialdepth completion and object detection tasks while fusing the data from the twosensors poses a challenge to resource efficiency. We address this challenge byproposing a novel approach to jointly execute the two tasks in a single pass.The proposed method is based on an encoder-focused multi-task learning modelthat exposes the two tasks to jointly learned features. We demonstrate howsemantic expectations of the objects in the scene learned by the objectdetection pathway can boost the performance of the depth completion pathwaywhile placing the missing depth values. Experimental results show that theproposed multi-task network outperforms its single-task counterpart,particularly when exposed to defective inputs.

Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion