Current methods for 3D reconstruction and environmental mapping frequently face challenges in achieving high precision, highlighting the need for practical and effective solutions. In response to this issue, our study introduces FlyNeRF, a system integrating Neural Radiance Fields (NeRF) with drone-based data acquisition for high-quality 3D reconstruction. Utilizing unmanned aerial vehicle (UAV) for capturing images and corresponding spatial coordinates, the obtained data is subsequently used for the initial NeRF-based 3D reconstruction of the environment. Further evaluation of the reconstruction render quality is accomplished by the image evaluation neural network developed within the scope of our system. According to the results of the image evaluation module, an autonomous algorithm determines the position for additional image capture, thereby improving the reconstruction quality. The neural network introduced for render quality assessment demonstrates an accuracy of 97%. Furthermore, our adaptive methodology enhances the overall reconstruction quality, resulting in an average improvement of 2.5 dB in Peak Signal-to-Noise Ratio (PSNR) for the 10% quantile. The FlyNeRF demonstrates promising results, offering advancements in such fields as environmental monitoring, surveillance, and digital twins, where high-fidelity 3D reconstructions are crucial.

翻译：当前的三维重建与环境测绘方法常面临高精度实现难题，亟需实用高效的解决方案。针对该问题，本研究提出FlyNeRF系统，将神经辐射场（NeRF）与无人机数据采集相结合，实现高质量三维重建。通过无人机（UAV）采集图像及对应空间坐标，所得数据用于基于NeRF的初始环境三维重建。系统内置图像评估神经网络对重建渲染质量进行后续评估。根据图像评估模块的输出结果，自主算法确定补充图像采集位置，从而提升重建质量。用于渲染质量评估的神经网络准确率达97%。此外，自适应方法整体提升了重建质量，使峰值信噪比（PSNR）的10%分位数平均提升2.5dB。FlyNeRF在环境监测、安防监控和数字孪生等对高保真三维重建要求严苛的领域展现出显著应用前景。