LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
CoRR(2023)
Abstract
Recent advancements in real-time neural rendering using point-based
techniques have paved the way for the widespread adoption of 3D
representations. However, foundational approaches like 3D Gaussian Splatting
come with a substantial storage overhead caused by growing the SfM points to
millions, often demanding gigabyte-level disk space for a single unbounded
scene, posing significant scalability challenges and hindering the splatting
efficiency.
To address this challenge, we introduce LightGaussian, a novel method
designed to transform 3D Gaussians into a more efficient and compact format.
Drawing inspiration from the concept of Network Pruning, LightGaussian
identifies Gaussians that are insignificant in contributing to the scene
reconstruction and adopts a pruning and recovery process, effectively reducing
redundancy in Gaussian counts while preserving visual effects. Additionally,
LightGaussian employs distillation and pseudo-view augmentation to distill
spherical harmonics to a lower degree, allowing knowledge transfer to more
compact representations while maintaining reflectance. Furthermore, we propose
a hybrid scheme, VecTree Quantization, to quantize all attributes, resulting in
lower bitwidth representations with minimal accuracy losses.
In summary, LightGaussian achieves an averaged compression rate over 15x
while boosting the FPS from 139 to 215, enabling an efficient representation of
complex scenes on Mip-NeRF 360, Tank and Temple datasets.
Project website: https://lightgaussian.github.io/
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