将3D高斯点按频率分组，实现更清晰的细节分离与高效渲染。
Frequency-Aware Gaussian Splatting Decomposition
- 按拉普拉斯金字塔频带分组3D高斯，每组专注特定频率
- 高频组用带符号残差颜色还原细微纹理，重建质量领先
- 支持动态细节渲染、渐进传输和焦点控制，适合交互应用
3D高斯点阵（3D-GS）实现了高效的新型视图合成，但对所有频率一视同仁，难以区分粗结构与细粒度细节。现有工作虽尝试利用频率信号，却未在3D表示层面进行显式频率分解。本文提出一种频率感知分解方法，将3D高斯点按输入图像的拉普拉斯金字塔子带分组。每组通过空间频率正则化训练，限制其分布于目标频带；高频组采用带符号残差颜色，以捕捉低频重建可能遗漏的精细细节。采用由粗到细的渐进式训练策略，提升分解稳定性。本方法在所有支持层级细节（LOD）的方法中达到最优的重建质量与渲染速度。除提升可解释性外，还支持动态细节渲染、渐进式流传输、中心视觉渲染、可提示的3D焦点控制及艺术化滤镜。代码将公开发布。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3D-GS) enables efficient novel view synthesis, but treats all frequencies uniformly, making it difficult to separate coarse structure from fine detail. Recent works have started to exploit frequency signals, but lack explicit frequency decomposition of the 3D representation itself. We propose a frequency-aware decomposition that organizes 3D Gaussians into groups corresponding to Laplacian-pyramid subbands of the input images. Each group is trained with spatial frequency regularization to confine it to its target frequency, while higher-frequency bands use signed residual colors to capture fine details that may be missed by lower-frequency reconstructions. A progressive coarse-to-fine training schedule stabilizes the decomposition. Our method achieves state-of-the-art reconstruction quality and rendering speed among all LOD-capable methods. In addition to improved interpretability, our method enables dynamic level-of-detail rendering, progressive streaming, foveated rendering, promptable 3D focus, and artistic filtering. Our code will be made publicly available.
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