FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering

ACM Transactions on Graphics (SIGGRAPH 2025)

Yunji Seo*, Young Sun Choi*, Hyun Seung Son, Youngjung Uh
Yonsei University

The videos above demonstrate the results of 3DGS and our method (3DGS-FLoD) running on a laptop equipped with an NVIDIA GeForce MX250 2GB GPU.
Our method enables customizable and memory-efficient rendering across various hardware configurations.

Abstract

3D Gaussian Splatting (3DGS) and its subsequent works are restricted to specific hardware setups, either on only low-cost or on only high-end configurations. Approaches aimed at reducing 3DGS memory usage enable rendering on low-cost GPU but compromise rendering quality, which fails to leverage the hardware capabilities in the case of higher-end GPU. Conversely, methods that enhance rendering quality require high-end GPU with large VRAM, making such methods impractical for lower-end devices with limited memory capacity.

To overcome this limitation, we propose Flexible Level of Detail (FLoD) for 3DGS. FLoD constructs a multi-level 3DGS representation through level-specific 3D scale constraints, where each level independently reconstructs the entire scene with varying detail and GPU memory usage. A level-by-level training strategy is introduced to ensure structural consistency across levels. Furthermore, the multi-level structure of FLoD allows selective rendering of image regions at different detail levels, providing additional memory-efficient rendering options. To our knowledge, among prior works which incorporate the concept of Level of Detail (LoD) with 3DGS, FLoD is the first to follow the core principle of LoD by offering adjustable options for a broad range of GPU settings.

Experiments demonstrate that FLoD provides various rendering options with trade-offs between quality and memory usage, enabling real-time rendering under diverse memory constraints. Furthermore, we show that FLoD generalizes to different 3DGS frameworks, indicating its potential for integration into future state-of-the-art developments.

Method Overview

Training starts at level 1 with the given SfM points and proceeds to the maximum level. Each level's training includes applying a scale constraint to provide the appropriate detail for that level and performing overlap pruning to mitigate Gaussian overlap. Upon completing each level’s training, Gaussian clones are saved to create a multi-level Gaussian set. This set enables both high-quality rendering using the maximum level and efficient rendering through selective rendering by using multiple levels.

Rendering Different Levels

Our method maintains the overall structure while providing level-appropriate details across each level.

Selective Rendering

Selecive rendering using levels 5, 4, and 3 from 3DGS-FLoD achieves visual quality comparable to using only level 5, while reducing the number of Gaussians by 40%.

Level5
Level(5,4,3)
Level5
Level(5,4,3)
Level5
Level(5,4,3)
Level5
Level(5,4,3)

Visual Comparisons

Comparison of the rendering results between Level 5 of 3DGS-FLoD and baseline models.

Ours
3DGS
Ours
Mip-Splatting
Ours
Scaffold-GS
Ours
Octree-GS

BibTeX

@article{seo2025flod,
      author  = {Yunji Seo and Young Sun Choi and Hyun Seung Son and Youngjung Uh},
      title   = {FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering},
      journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH)},
      number  = {4},
      volume  = {44},
      year    = {2025},
      doi     = {10.1145/3731430}
}