Abstract: In order to better apply NeRF high-precision 3D modeling in the 3D digital reconstruction of urban real scenes,this paper divides the large scene into sub-NeRF based on NeRF technology,and initializes the polygon mesh by constructing multiple octahedral bodies in the scene. And the vertices of the polygon faces are continuously optimized during the training process. After the training is completed,the weights of the encoder-decoder network are obtained,and different levels of polygon mesh refinement are performed on each independent block through vertex optimization. From satellite-level images that capture city overviews to ground-level images that show complex details of buildings,multi-scale data for urban detail and spatial coverage are constructed through progressive learning. The neural network voxel rendering model uses a multilayer perceptron (MLP) to realize the parameterization of volume density and color,and uses a hierarchical sampling method to realize the sorting distance vector of rays between the near plane and the far plane of a predefined viewing angle,so as to realize real-time interactive rendering of large-scale scenes. Then,GIS and NeRF are fused to provide a new solution for tasks such as multi-data fusion,query,analysis,measurement,annotation and sharing,so as to achieve instant and smooth dragging,zooming and 360° browsing and viewing of scenes without dead ends. This fusion makes it easy to integrate various data sources for spatial analysis in 3D scenarios such as urban planning,land management and environmental monitoring.

Key words: 3D modeling, NeRF neural radiance field, training, rendering