EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS

arXiv:2312.04564v2 Announce Type: replace
Abstract: 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 …

arxiv cs.cv cs.gr type