用统一框架重建大场景，兼顾全局一致性与局部细节。
Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
- 将相机位姿与高斯属性融合为视图感知表示，整体建模全场景。
- 在大型场景上实现最优渲染质量，单卡24GB显存即可训练。
- 适合需要高效、高质量3D重建的工业应用与科研项目。
近年来，3D高斯溅射在新视角合成方面展现出巨大潜力。然而，现有大规模场景重建方法多采用分而治之范式，常导致全局信息丢失，并因场景分割和局部优化带来复杂的参数调优问题。为此，我们提出MixGS，一种全新的全场景优化框架用于大规模3D场景重建。MixGS通过将相机位姿与高斯属性整合到视图感知表示中，整体建模整个场景，并解码生成精细高斯；此外，创新的混合操作结合解码后与原始高斯，共同保持全局连贯性与局部保真度。大量实验表明，MixGS在大型场景上实现了最先进的渲染质量，同时具备良好速度表现，显著降低计算开销，可在单张24GB显存的GPU上完成大规模场景重建训练。代码将发布于https://github.com/azhuantou/MixGS。
原文摘要 · Abstract (English)
Recent advances in 3D Gaussian Splatting have shown remarkable potential for novel view synthesis. However, most existing large-scale scene reconstruction methods rely on the divide-and-conquer paradigm, which often leads to the loss of global scene information and requires complex parameter tuning due to scene partitioning and local optimization. To address these limitations, we propose MixGS, a novel holistic optimization framework for large-scale 3D scene reconstruction. MixGS models the entire scene holistically by integrating camera pose and Gaussian attributes into a view-aware representation, which is decoded into fine-detailed Gaussians. Furthermore, a novel mixing operation combines decoded and original Gaussians to jointly preserve global coherence and local fidelity. Extensive experiments on large-scale scenes demonstrate that MixGS achieves state-of-the-art rendering quality and competitive speed, while significantly reducing computational requirements, enabling large-scale scene reconstruction training on a single 24GB VRAM GPU. The code will be released at https://github.com/azhuantou/MixGS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。