LODGE Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering

Jonas Kulhanek Google
Marie-Julie Rakotosaona Google
Fabian Manhardt Google
Christina Tsalicoglou Google
Michael Niemeyer Google
Torsten Sattler CTU in Prague
Songyou Peng Google DeepMind
Federico Tombari Google

LODGE enables efficient rendering of large-scale 3DGS even on mobile devices

Abstract

LODGE overview
Left (LOD): The scene is represented with multiple LODs; `active Gaussians' are selected during training based on camera distance. Right (cluster-based rendering): We cluster cameras into chunks, pre-compute `active Gaussians' per chunk, and render the two nearest chunks with `opacity blending'.
Ours
FPS: 257
ZipNeRF
FPS: 0.09
Ours
FPS: 219
Octree-GS
FPS: 119
Ours
FPS: 280
H3DGS
FPS: 33
Ours
FPS: 253
3DGS
FPS: 99

LOD representation for faster rendering

We show the number of visible Gaussians per pixel (lighter means more Gaussians). By adding the LOD representation with chunk-based rendering, we reduce the number of visible Gaussians per pixel, significantly improving the rendering speed with little loss in quality.

Ours
FPS: 258
Full representation
FPS: 66

Opacity interpolation to remove cross-chunk transitions

To ensure temporal consistency when transitioning between chunks, we propose opacity blending. Please focus on the black car. Notice, how opacity blending removes the sharp changes visible in LOD + chunks.

Ours
FPS: 258
LOD + chunks
FPS: 317

Citation

Please use the following citation:
@article{kulhanek2025lodge
  title={{LODGE}: Level-of-Detail Large-Scale {G}aussian Splatting with Efficient Rendering}, 
  author={Jonas Kulhanek and Marie-Julie Rakotosaona and Fabian Manhardt and Christina Tsalicoglou and Michael Niemeyer and Torsten Sattler and Songyou Peng and Federico Tombari},
  year={2025},
  journal={arXiv},
}