City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
This enables real-time, high-quality visualization of large-scale scenes on web platforms, addressing a bottleneck for applications like virtual tours or urban planning, though it is incremental in extending existing methods to larger scales.
The paper tackles the problem of real-time neural rendering of large-scale scenes on the web, which is challenging due to limited resources, and achieves approximately 32FPS with RTX 3060 GPU while maintaining rendering quality comparable to state-of-the-art methods.
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.