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

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

URL: http://arxiv.org/abs/2312.16457v2
Date: Mon, 1 Apr 2024 03:10:53 GMT
Title: City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
Authors: Kaiwen Song, Xiaoyi Zeng, Chenqu Ren, Juyong Zhang,
Abstract summary: City-on-Web is the first method for real-time rendering of large-scale scenes on the web. Our system achieves real-time rendering of large-scale scenes at approximately 32FPS with GTX 3060 GPU on the web.
Score: 26.92522314818356
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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