  Existing neural radiance field-based methods can achieve real-time rendering
of small scenes on the web platform. However, extending these methods to
large-scale scenes still poses significant challenges due to limited resources
in computation, memory, and bandwidth. In this paper, we propose City-on-Web,
the first method for real-time rendering of large-scale scenes on the web. We
propose a block-based volume rendering method to guarantee 3D consistency and
correct occlusion between blocks, and introduce a Level-of-Detail strategy
combined with dynamic loading/unloading of resources to significantly reduce
memory demands. Our system achieves real-time rendering of large-scale scenes
at approximately 32FPS with RTX 3060 GPU on the web and maintains rendering
quality comparable to the current state-of-the-art novel view synthesis
methods.
