To address the challenges faced by neural radiance field(NeRF)technology in the 3D reconstruction of complex architectural scenes

such as long training time and limited detail description

the Structure-NeRF method is proposed.Multi-view sensor data are acquired using unmanned aerial vehicle aerial photography.This method combines multi-sampling

weight reduction

and loss function optimization to reduce aliasing.Geometric constraints(normal consistency

plane fitting

and vertical/horizontal constraints)are introduced to improve reconstruction accuracy.Furthermore

the Manhattan World Model(MWM) is used to accurately extract the main structure and dimensions of buildings.Experimental results show that Structure-NeRF achieves a peak signal-to-noise ratio(PSNR) of 36.23dB

reduces training time by 50%

and its structural similarity index(SSIM)is improved by 13.41% and 50.00% compared with the mainstream models mip-NeRF and iNGP

respectively

while its PSNR is increased by 10.45% and 25.10%

respectively.Structure-NeRF significantly improves training efficiency and rendering quality

achieving high-precision 3D reconstruction of architectural spatial models.