EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
Sharath Girish Kamal Gupta Abhinav Shrivastava
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