FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering

• URL: http://arxiv.org/abs/2408.12894v1
• Date: Fri, 23 Aug 2024 07:56:25 GMT
• Title: FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering
• Authors: Yunji Seo, Young Sun Choi, Hyun Seung Son, Youngjung Uh,
• Abstract summary: 3D Gaussian Splatting (3DGS) achieves fast and high-quality renderings by using numerous small Gaussians. This reliance on a large number of Gaussians restricts the application of 3DGS-based models on low-cost devices due to memory limitations. We propose integrating a Flexible Level of Detail (FLoD) to 3DGS, to allow a scene to be rendered at varying levels of detail according to hardware capabilities.
• Score: 8.838958391604175
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) achieves fast and high-quality renderings by using numerous small Gaussians, which leads to significant memory consumption. This reliance on a large number of Gaussians restricts the application of 3DGS-based models on low-cost devices due to memory limitations. However, simply reducing the number of Gaussians to accommodate devices with less memory capacity leads to inferior quality compared to the quality that can be achieved on high-end hardware. To address this lack of scalability, we propose integrating a Flexible Level of Detail (FLoD) to 3DGS, to allow a scene to be rendered at varying levels of detail according to hardware capabilities. While existing 3DGSs with LoD focus on detailed reconstruction, our method provides reconstructions using a small number of Gaussians for reduced memory requirements, and a larger number of Gaussians for greater detail. Experiments demonstrate our various rendering options with tradeoffs between rendering quality and memory usage, thereby allowing real-time rendering across different memory constraints. Furthermore, we show that our method generalizes to different 3DGS frameworks, indicating its potential for integration into future state-of-the-art developments. Project page: https://3dgs-flod.github.io/flod.github.io/

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