Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications

TL;DR

This paper evaluates neural 3D reconstruction techniques integrated with small UAVs, demonstrating significant improvements in 3D mapping quality for static objects in constrained environments.

Contribution

It introduces a novel N3DR pipeline using advanced models for small UAVs, enhancing 3D reconstruction quality over traditional SfM methods.

Findings

N3DR models outperform SfM in reconstruction quality

The pipeline enables high-precision 3D mapping with small UAVs

Experimental results confirm feasibility in constrained environments

Abstract

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.