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.
@article{arxiv.2312.16457,
title = {City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web},
author = {Kaiwen Song and Xiaoyi Zeng and Chenqu Ren and Juyong Zhang},
journal= {arXiv preprint arXiv:2312.16457},
year = {2024}
}