Algorithm for Generating Orthophotos from Unmanned Aerial Vehicle Imagery Based on Neural Radiance Fields

Digital orthophotos are renowned for their high geometric accuracy and distortion-free characteristics, captured from a parallel perspective. They are widely used in map making, urban planning, and related fields. In this study, we employed the neural radiance field (NeRF) technology to generate highly realistic orthophotos through an end-to-end image generation process, eliminating the need for prior 3D geometric information or auxiliary data. We compared the NeRF-based approach with current mainstream photogrammetric methods for orthophoto generation. The experimental results demonstrate that the NeRF-based algorithm meets the measurement accuracy requirements and surpasses traditional methods in terms of detail and texture quality. We analyzed the performance characteristics of forward-facing and 360 degrees objectcentric camera shooting methods. Our findings indicate that combining these techniques yields high-quality orthophotos, demonstrating the advantages of implicit methods in orthophoto generation. Moreover, the findings provide valuable guidance for the efficient production of digital orthophotos.