Three-Dimensional Reconstruction of Cities Based on Neural Radiation Fields
Urban 3D reconstruction is a hot issue that has received much attention in the field of computer vision.The wide application of ur-ban 3D modeling covers many fields,however,there are still some problems in the current work of urban scene reconstruction.The problem of data multi-scale in urban scenes,the reconstruction of the near scene is blurred while the reconstruction of the far scene appears jagged,and the details of the far scene are under-represented and the edges are blurred.In order to solve these problems,a staged 3D reconstruction meth-od based on neural radiation field is used and a sampling distribution strategy based on far boundary is proposed.The staged reconstruction method allows the model to learn the city scene layer by layer from far and near,which solves the multi-scale problem of city modeling.The sampling strategy is able to capture the details of the scene area more effectively by calculating the light distribution and sampling densely on the far boundary,which helps the model to learn and express the nuances of the urban scene more comprehensively and restore the details in the far boundary more accurately.Comparison in the experiment reveals that the image PSNR increased by 7.22%,SSIM increased by 17.20%,and LPIPS decreased by 32.40%,indicating that the method can effectively improve the rendering quality.
neural radiation field3D reconstructionurban scenesampling strategymulti-scale data
国家科技期刊平台
NSTL
万方数据
