通过分层细节控制，实现在低内存设备上高效渲染大规模3D场景。
LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
- 基于相机距离动态选择高斯点子集，构建多级细节层次。
- 在室外和室内数据集上实现更优的渲染质量与更低延迟。
- 分块加载结合透明度融合，避免边界伪影，节省显存。
本文提出一种面向3D高斯溅射的新型分层细节（LOD）方法，可在内存受限设备上实现实时渲染大规模场景。该方法通过迭代选择基于相机距离的最优高斯点子集，显著降低渲染时间和GPU显存占用。每个LOD层级通过深度感知的3D平滑滤波、基于重要性的剪枝与微调构建，以保持视觉保真度。为进一步减少内存开销，将场景划分为空间块，动态加载相关高斯点，并采用透明度融合机制防止块间出现视觉伪影。在室外（Hierarchical 3DGS）和室内（Zip-NeRF）数据集上均达到当前最佳性能，实现高质量渲染的同时降低延迟与资源消耗。
原文摘要 · Abstract (English)
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.
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