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.

Octree-GS 是一种基于层次细节控制的 3D 高斯方法，解决了 3D-GS 在大场景、复杂细节和不同尺度下的渲染限制，并实现了实时渲染性能和高保真度的平衡。

Octree-GS：基于 LOD 结构化三维高斯函数的一致实时渲染

Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D  Gaussians

The recent proliferation of 3D content that can be consumed on hand-held
devices necessitates efficient tools for transmitting large geometric data,
e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a
challenge to storage as well as transmission bandwidth, and level-of-detail
techniques are often used to transmit an asset using an appropriate bandwidth
budget. It is especially desirable for these methods to transmit data
progressively, improving the quality of the geometry with more data. Our key
insight is that the geometric details of 3D meshes often exhibit similar local
patterns even across different shapes, and thus can be effectively represented
with a shared learned generative space. We learn this space using a
subdivision-based encoder-decoder architecture trained in advance on a large
collection of surfaces. We further observe that additional residual features
can be transmitted progressively between intermediate levels of subdivision
that enable the client to control the tradeoff between bandwidth cost and
quality of reconstruction, providing a neural progressive mesh representation.
We evaluate our method on a diverse set of complex 3D shapes and demonstrate
that it outperforms baselines in terms of compression ratio and reconstruction
quality.

通过使用分级细节技术和渐进式网格表示方法，在大量三维几何数据的传输过程中提高质量并降低带宽成本。