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

2025-06-24

AI Summary

To address the low 3D reconstruction accuracy and weak autonomy of small-scale UAVs in indoor and constrained environments—stemming from stringent dynamical and power constraints—this paper proposes a neural radiance field (NeRF) reconstruction framework tailored for small-scale static objects. We pioneer the integration of state-of-the-art NeRF models—including Instant-NGP, Nerfacto, and Splatfacto—onto lightweight UAV platforms, establishing an end-to-end, multi-UAV collaborative imaging and reconstruction pipeline (N3DR). Compared to conventional Structure-from-Motion (SfM) approaches, N3DR achieves significant improvements in quantitative metrics: higher PSNR and SSIM for rendered images, and lower Chamfer Distance (CD) and higher F-Score for reconstructed point clouds. The framework delivers superior spatial resolution, enhanced geometric fidelity, and improved robustness in detecting anomalous structures. This work establishes a novel paradigm for high-fidelity 3D digitization under severe resource constraints.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Machine Learning: Learning with Manifolds

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Enhancing 3D reconstruction for small UAVs in constrained environments
• Overcoming flight dynamics and power limitations with Neural 3D Reconstruction
• Improving reconstruction quality using advanced models for precise mapping

Innovation

• Integrates Neural 3D Reconstruction with small UAVs
• Uses Instant-ngp, Nerfacto, and Splatfacto models
• Enhances 3D mapping and anomaly detection precision