Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels.We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour.
近年来，新视角合成取得了重大进展，其中三维高斯喷洒提供了卓越的视觉质量、快速训练和实时渲染。然而，训练和渲染所需的资源不可避免地限制了能够以良好视觉质量表示的捕获场景的大小。我们引入了一个三维高斯的层次结构，它保持了非常大型场景的视觉质量，同时提供了一个有效的细节级别（LOD）解决方案，用于高效渲染远处内容，并有效选择级别和平滑过渡各级别。我们引入了一种分而治之的方法，允许我们在独立的块中训练非常大的场景。我们将这些块整合成一个层次结构，可以优化以进一步提高合并到中间节点的高斯的视觉质量。非常大的捕获场景通常具有场景的稀疏覆盖，对原始的三维高斯喷洒训练方法提出了许多挑战；我们调整并规范训练以解决这些问题。我们提供了一个完整的解决方案，使得实时渲染非常大的场景成为可能，并能够根据我们的LOD方法适应可用资源。我们展示了使用一个简单且经济的装置捕获的场景的结果，这些场景包含多达数万张图片，覆盖长达数公里的轨迹，持续时间长达一小时。