City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916185345359872 |
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| author | Song, Kaiwen Zeng, Xiaoyi Ren, Chenqu Zhang, Juyong |
| author_facet | Song, Kaiwen Zeng, Xiaoyi Ren, Chenqu Zhang, Juyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16457 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web Song, Kaiwen Zeng, Xiaoyi Ren, Chenqu Zhang, Juyong Computer Vision and Pattern Recognition Graphics 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. |
| title | City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2312.16457 |