Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large
  Scene Reconstruction
Authors: name: Jixuan Fan, name: Wanhua Li, name: Yifei Han, name: Yansong Tang

3D Gaussian Splatting has demonstrated notable success in large-scale scene
reconstruction, but challenges persist due to high training memory consumption
and storage overhead. Hybrid representations that integrate implicit and
explicit features offer a way to mitigate these limitations. However, when
applied in parallelized block-wise training, two critical issues arise since
reconstruction accuracy deteriorates due to reduced data diversity when
training each block independently, and parallel training restricts the number
of divided blocks to the available number of GPUs. To address these issues, we
propose Momentum-GS, a novel approach that leverages momentum-based
self-distillation to promote consistency and accuracy across the blocks while
decoupling the number of blocks from the physical GPU count. Our method
maintains a teacher Gaussian decoder updated with momentum, ensuring a stable
reference during training. This teacher provides each block with global
guidance in a self-distillation manner, promoting spatial consistency in
reconstruction. To further ensure consistency across the blocks, we incorporate
block weighting, dynamically adjusting each block's weight according to its
reconstruction accuracy. Extensive experiments on large-scale scenes show that
our method consistently outperforms existing techniques, achieving a 12.8%
improvement in LPIPS over CityGaussian with much fewer divided blocks and
establishing a new state of the art. Project page:
https://jixuan-fan.github.io/Momentum-GS_Page/