Adaptive Flight Planning for Neural 3D Reconstruction With Low-Altitude UAVs

Contemporary advances in the Neural 3 Dimensional (3D) Reconstruction (N3DR) have enabled new possibilities for detailed scene understanding and immersive environment modeling. However, the quality of reconstruction remains heavily sensitive to viewpoint selection and flight trajectory, particularly in low-altitude Unmanned Aerial Vehicle (UAV) applications, where energy, time, and occlusions pose significant challenges. This article presents an intelligent and adaptive path planning framework that dynamically optimizes UAVs’ viewpoints to maximize neural reconstruction quality while respecting real-world constraints. Unlike static or heuristic approaches, our system integrates online feedback from the reconstruction engine to guide UAVs toward underexplored or uncertain regions in real time. We review key principles of view planning, identify limitations of current strategies, and introduce a modular framework capable of fusing scene priors, uncertainty maps, and flight constraints into trajectory optimization. Finally, we describe an experimental evaluation methodology that leverages a real-world testbed deployment and synthetic benchmarks to validate the effectiveness of dynamic planning on both reconstruction accuracy and operational efficiency. This work aims to bridge aerial robotics, Internet of Things (IoT)-enabled vision systems, and Artificial Intelligence (AI)-driven planning for robust 3 Dimensional (3D) mapping in complex environments.