Lightweight Predictive 3D Gaussian Splats

TL;DR

This paper introduces a compact 3D Gaussian splat representation that significantly reduces storage requirements while maintaining or improving rendering quality, enabling real-time rendering on mobile devices.

Contribution

A novel representation method that shares attributes among nearby points and predicts discarded points with tiny MLPs, reducing storage without sacrificing quality.

Findings

Achieves high-quality rendering with much less storage.

Enables real-time rendering on mobile devices.

Outperforms existing compact solutions in quality and efficiency.

Abstract

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.