Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach results in scene representations with fewer Gaussians and quantized representations, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce memory by more than an order of magnitude all while maintaining the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x less memory and faster training/inference speed. Project page and code is available https://efficientgaussian.github.io

翻译：最近，三维高斯泼溅（3D-GS）在新视角场景合成中受到广泛关注。该方法通过快速可微的三维高斯光栅化，解决了神经辐射场（NeRF）训练时间长和渲染速度慢的问题，实现了实时渲染和加速训练。然而，由于每个场景的点云表示需要数百万个高斯体，该方法在训练和存储过程中需要大量内存资源。本文提出一种利用量化嵌入大幅降低内存存储需求的技术，并采用由粗到精的训练策略实现高斯点云更快速稳定的优化。我们的方法通过更少的高斯体和量化表示实现场景重构，从而在高分辨率场景实时渲染中缩短训练时间并提升渲染速度。在保持重建质量的前提下，我们将内存消耗降低了一个数量级以上。我们在多个数据集和场景上验证了该方法的效果——在保持视觉质量的同时，内存消耗降低10-20倍，训练/推理速度更快。项目主页和代码参见：https://efficientgaussian.github.io