Small Unmanned Aerial Vehicles (UAVs) exhibit immense potential for navigating indoor and hard-to-reach areas, yet their significant constraints in payload and autonomy have largely prevented their use for complex tasks like high-quality 3-Dimensional (3D) reconstruction. To overcome this challenge, we introduce a novel system architecture that enables fully autonomous, high-fidelity 3D scanning of static objects using UAVs weighing under 100 grams. Our core innovation lies in a dual-reconstruction pipeline that creates a real-time feedback loop between data capture and flight control. A near-real-time (near-RT) process uses Structure from Motion (SfM) to generate an instantaneous pointcloud of the object. The system analyzes the model quality on the fly and dynamically adapts the UAV's trajectory to intelligently capture new images of poorly covered areas. This ensures comprehensive data acquisition. For the final, detailed output, a non-real-time (non-RT) pipeline employs a Neural Radiance Fields (NeRF)-based Neural 3D Reconstruction (N3DR) approach, fusing SfM-derived camera poses with precise Ultra Wide-Band (UWB) location data to achieve superior accuracy. We implemented and validated this architecture using Crazyflie 2.1 UAVs. Our experiments, conducted in both single- and multi-UAV configurations, conclusively show that dynamic trajectory adaptation consistently improves reconstruction quality over static flight paths. This work demonstrates a scalable and autonomous solution that unlocks the potential of miniaturized UAVs for fine-grained 3D reconstruction in constrained environments, a capability previously limited to much larger platforms.

翻译：小型无人机在室内及难以到达区域导航方面展现出巨大潜力，然而其在有效载荷和自主性方面的显著限制，很大程度上阻碍了其用于高质量三维重建等复杂任务。为克服这一挑战，我们提出一种新颖的系统架构，能够使用重量低于100克的无人机对静态物体进行全自主、高保真度的三维扫描。我们的核心创新在于采用双重建流水线，在数据采集与飞行控制之间建立实时反馈循环。一个近实时处理流程利用运动恢复结构技术生成物体的即时点云。系统动态分析模型质量，并自适应调整无人机轨迹，智能采集覆盖不足区域的新图像，从而确保数据采集的全面性。对于最终精细输出，非实时流水线采用基于神经辐射场的神经三维重建方法，将运动恢复结构导出的相机姿态与精确的超宽带定位数据相融合，以实现卓越的精度。我们使用Crazyflie 2.1无人机实现并验证了该架构。在单机与多机配置下进行的实验一致表明，动态轨迹自适应相较于静态飞行路径能持续提升重建质量。这项工作展示了一种可扩展的自主解决方案，释放了微型无人机在受限环境中进行细粒度三维重建的潜力，该能力以往仅限于更大型平台。