Recent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representations is indeed extremely efficient, storing and transmitting them is often prohibitively expensive. To represent large-scale scenes, one often needs to store millions of 3D Gaussians, occupying gigabytes of disk space. This poses a very practical limitation, prohibiting widespread adoption.Several solutions have been proposed to strike a balance between disk size and rendering quality, noticeably reducing the visual quality. In this work, we propose a new representation that dramatically reduces the hard drive footprint while featuring similar or improved quality when compared to the standard 3D Gaussian splats. When compared to other compact solutions, ours offers higher quality renderings with significantly reduced storage, being able to efficiently run on a mobile device in real-time. Our key observation is that nearby points in the scene can share similar representations. Hence, only a small ratio of 3D points needs to be stored. We introduce an approach to identify such points which are called parent points. The discarded points called children points along with attributes can be efficiently predicted by tiny MLPs.
近年来，使用高斯斑点表示3D对象和场景的方法在各种平台和设备上显示出了更快的渲染速度。尽管渲染这种表示确实非常高效，但存储和传输却往往代价高昂。为了表示大规模场景，通常需要存储数百万个3D高斯函数，占据几十GB的磁盘空间。这对实际应用构成了严重限制，阻碍了广泛采用。 已经提出了几种解决方案来在磁盘空间和渲染质量之间取得平衡，明显降低了视觉质量。在本研究中，我们提出了一种新的表示方法，显著减少了硬盘占用空间，同时在与标准3D高斯斑点相比的视觉质量方面表现出类似或更好的效果。与其他紧凑方案相比，我们的方法能够以更高质量的渲染效果显著减少存储空间，可以在移动设备上实时高效运行。 我们的关键观察是场景中附近的点可以共享类似的表示。因此，只需要存储场景中的少量3D点。我们引入了一种方法来识别这些称为父点的点。被丢弃的点，即称为子点的点，以及相关属性可以通过小型MLP（多层感知器）进行高效预测。