The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing frustum. This limitation is particularly noticeable in zoom-out views and can lead to inconsistent rendering speeds in scenes with varying details. Moreover, it often struggles to capture the corresponding level of details at different scales with its heuristic density control operation. Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results. Our model dynamically selects the appropriate level from the set of multi-resolution anchor points, ensuring consistent rendering performance with adaptive LOD adjustments while maintaining high-fidelity rendering results.

翻译：近期提出的3D高斯泼溅（3D-GS）相较于基于NeRF的神经场景表征方法，在渲染保真度和效率方面展现出显著优势。尽管具备实时渲染潜力，但3D-GS在大尺度复杂细节场景中，由于视锥体内存在过量高斯基元，导致渲染瓶颈。此限制在缩览视图中尤为明显，且易造成不同细节场景的渲染速度不一致。此外，其启发式密度控制操作难以在不同尺度下捕获对应细节层级。受细节层次（LOD）技术启发，我们提出Octree-GS，该方案采用LOD结构的3D高斯方法，支持场景表示的细节层次分解以优化最终渲染结果。模型通过从多分辨率锚点集合中动态选择适应当前尺度的层级，在保持高保真渲染效果的同时，通过自适应LOD调整确保一致的渲染性能。