Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications
This work addresses the problem of high-precision 3D mapping and anomaly detection in constrained environments for small UAV applications, representing an incremental advancement by applying existing N3DR models to this domain.
The paper tackled the challenge of limited autonomy and mission capabilities in small UAVs by integrating Neural 3D Reconstruction (N3DR) with UAV systems for fine-grained 3D digital reconstruction of static objects, resulting in significantly improved reconstruction quality compared to a baseline Structure from Motion algorithm.
The increasing miniaturization of Unmanned Aerial Vehicles (UAVs) has expanded their deployment potential to indoor and hard-to-reach areas. However, this trend introduces distinct challenges, particularly in terms of flight dynamics and power consumption, which limit the UAVs' autonomy and mission capabilities. This paper presents a novel approach to overcoming these limitations by integrating Neural 3D Reconstruction (N3DR) with small UAV systems for fine-grained 3-Dimensional (3D) digital reconstruction of small static objects. Specifically, we design, implement, and evaluate an N3DR-based pipeline that leverages advanced models, i.e., Instant-ngp, Nerfacto, and Splatfacto, to improve the quality of 3D reconstructions using images of the object captured by a fleet of small UAVs. We assess the performance of the considered models using various imagery and pointcloud metrics, comparing them against the baseline Structure from Motion (SfM) algorithm. The experimental results demonstrate that the N3DR-enhanced pipeline significantly improves reconstruction quality, making it feasible for small UAVs to support high-precision 3D mapping and anomaly detection in constrained environments. In more general terms, our results highlight the potential of N3DR in advancing the capabilities of miniaturized UAV systems.