基于混合高斯泼溅的整体大规模场景重建
Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
AI总结：
针对现有大规模场景重建分治范式丢失全局信息且参数调优复杂的问题，提出整体优化框架MixGS，通过视角感知表示和混合操作结合解码与原始高斯，在单张24GB GPU上实现了最先进的渲染质量与竞争力速度。
摘要：
3D Gaussian Splatting的最新进展已展现出在新视角合成方面的显著潜力。然而，现有的大多数大规模场景重建方法依赖分治范式，由于场景划分和局部优化，这通常会导致全局场景信息的丢失，并需要复杂的参数调整。为了解决这些局限性，我们提出了MixGS，一种用于大规模3D场景重建的新型整体优化框架。MixGS通过将相机位姿和高斯属性整合到视角感知表示中，对整个场景进行整体建模，并将其解码为细节精细的高斯。此外，一种新颖的混合操作将解码后的高斯与原始高斯相结合，以共同保持全局一致性和局部保真度。在大规模场景上的大量实验表明，MixGS实现了最先进的渲染质量和具有竞争力的速度，同时显著降低了计算需求，使得在单张24GB VRAM GPU上进行大规模场景重建训练成为可能。代码将发布于https://github.com/azhuantou/MixGS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
