  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.
