title: City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
summary: NeRF has significantly advanced 3D scene reconstruction, capturing intricate
  details across various environments. Existing methods have successfully
  leveraged radiance field baking to facilitate real-time rendering of small
  scenes. However, when applied to large-scale scenes, these techniques encounter
  significant challenges, struggling to provide a seamless real-time experience
  due to limited resources in computation, memory, and bandwidth. In this paper,
  we propose City-on-Web, which represents the whole scene by partitioning it
  into manageable blocks, each with its own Level-of-Detail, ensuring high
  fidelity, efficient memory management and fast rendering. Meanwhile, we
  carefully design the training and inference process such that the final
  rendering result on web is consistent with training. Thanks to our novel
  representation and carefully designed training/inference process, we are the
  first to achieve real-time rendering of large-scale scenes in
  resource-constrained environments. Extensive experimental results demonstrate
  that our method facilitates real-time rendering of large-scale scenes on a web
  platform, achieving 32FPS at 1080P resolution with an RTX 3060 GPU, while
  simultaneously achieving a quality that closely rivals that of state-of-the-art
  methods. Project page: https://ustc3dv.github.io/City-on-Web/
paper_authors._id: 658e49e389f1ff04631b337d
paper_authors.name: Kaiwen Song
paper_authors._id: 658e49e389f1ff04631b337e
paper_authors.name: Juyong Zhang
paper_title: City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
paper_summary: NeRF has significantly advanced 3D scene reconstruction, capturing intricate
  details across various environments. Existing methods have successfully
  leveraged radiance field baking to facilitate real-time rendering of small
  scenes. However, when applied to large-scale scenes, these techniques encounter
  significant challenges, struggling to provide a seamless real-time experience
  due to limited resources in computation, memory, and bandwidth. In this paper,
  we propose City-on-Web, which represents the whole scene by partitioning it
  into manageable blocks, each with its own Level-of-Detail, ensuring high
  fidelity, efficient memory management and fast rendering. Meanwhile, we
  carefully design the training and inference process such that the final
  rendering result on web is consistent with training. Thanks to our novel
  representation and carefully designed training/inference process, we are the
  first to achieve real-time rendering of large-scale scenes in
  resource-constrained environments. Extensive experimental results demonstrate
  that our method facilitates real-time rendering of large-scale scenes on a web
  platform, achieving 32FPS at 1080P resolution with an RTX 3060 GPU, while
  simultaneously achieving a quality that closely rivals that of state-of-the-art
  methods. Project page: https://ustc3dv.github.io/City-on-Web/
paper_ai_keywords: NeRF radiance field baking real-time rendering Level-of-Detail high fidelity memory management large-scale scenes resource-constrained environments web platform RTX 3060 GPU