City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web

Kaiwen Song, Xiaoyi Zeng, Chenqu Ren, Juyong Zhang* ;

"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 accommodate the independent resource characteristics of web-based rendering, 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 32FPS with RTX 3060 GPU on the web and maintains quality comparable to the current state-of-the-art novel view synthesis methods. Project page: https://ustc3dv.github.io/City-on-Web/"