LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
This addresses the challenge of rendering large-scale 3D scenes efficiently for applications on devices with limited memory, representing an incremental improvement over existing 3D Gaussian Splatting methods.
The paper tackles the problem of real-time rendering of large-scale 3D scenes on memory-constrained devices by introducing a level-of-detail method for 3D Gaussian Splatting, achieving state-of-the-art performance with reduced latency and memory usage.
In this work, we present a novel level-of-detail (LOD) method for 3D Gaussian Splatting that enables real-time rendering of large-scale scenes on memory-constrained devices. Our approach introduces a hierarchical LOD representation that iteratively selects optimal subsets of Gaussians based on camera distance, thus largely reducing both rendering time and GPU memory usage. We construct each LOD level by applying a depth-aware 3D smoothing filter, followed by importance-based pruning and fine-tuning to maintain visual fidelity. To further reduce memory overhead, we partition the scene into spatial chunks and dynamically load only relevant Gaussians during rendering, employing an opacity-blending mechanism to avoid visual artifacts at chunk boundaries. Our method achieves state-of-the-art performance on both outdoor (Hierarchical 3DGS) and indoor (Zip-NeRF) datasets, delivering high-quality renderings with reduced latency and memory requirements.