City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
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/
658e49e389f1ff04631b337d
Kaiwen Song
658e49e389f1ff04631b337e
Juyong Zhang
City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
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/
City-on-Web achieves real-time rendering of large-scale 3D scenes on the web through partitioning and level-of-detail management, ensuring high fidelity and efficient resource usage.
NeRF radiance field real-time rendering level-of-detail resource-constrained environments