Spectrally Pruned Gaussian Fields with Neural Compensation
Authors: name: Runyi Yang; name: Zhenxin Zhu; name: Zhou Jiang; name: Baijun Ye; name: Xiaoxue Chen; name: Yifei Zhang; name: Yuantao Chen; name: Jian Zhao; name: Hao Zhao
Published: 2024-05-01 17:59:45

Abstract: Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered
attention for its fast rendering speed and high rendering quality. However,
this comes with high memory consumption, e.g., a well-trained Gaussian field
may utilize three million Gaussian primitives and over 700 MB of memory. We
credit this high memory footprint to the lack of consideration for the
relationship between primitives. In this paper, we propose a memory-efficient
Gaussian field named SUNDAE with spectral pruning and neural compensation. On
one hand, we construct a graph on the set of Gaussian primitives to model their
relationship and design a spectral down-sampling module to prune out primitives
while preserving desired signals. On the other hand, to compensate for the
quality loss of pruning Gaussians, we exploit a lightweight neural network head
to mix splatted features, which effectively compensates for quality losses
while capturing the relationship between primitives in its weights. We
demonstrate the performance of SUNDAE with extensive results. For example,
SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory while the vanilla
Gaussian splatting algorithm achieves 25.60 PSNR at 160 FPS using 523 MB
memory, on the Mip-NeRF360 dataset. Codes are publicly available at
https://runyiyang.github.io/projects/SUNDAE/.