Frequency-Aware Gaussian Splatting Decomposition
Tel Aviv University, 2025
3D Gaussian Splatting (3D-GS) has revolutionized novel view synthesis with its efficient, explicit representation. However, it lacks frequency interpretability, making it difficult to separate low-frequency structures from fine details. We introduce a frequency-decomposed 3D-GS framework that groups 3D Gaussians that correspond to subbands in the Laplacian Pyrmaids of the input images.
Our approach enforces coherence within each subband (i.e., group of 3D Gaussians) through dedicated regularization, ensuring well-separated frequency components. We extend color values to both positive and negative ranges, allowing higher-frequency layers to add or subtract residual details. To stabilize optimization, we employ a progressive training scheme that refines details in a coarse-to-fine manner.
Beyond interpretability, this frequency-aware design unlocks a range of practical benefits. Explicit frequency separation enables advanced 3D editing and stylization, allowing precise manipulation of specific frequency bands. It also supports dynamic level-of-detail control for progressive rendering, streaming, foveated rendering and fast geometry interaction. Through extensive experiments, we demonstrate that our method provides improved control and flexibility for emerging applications in scene editing and interactive rendering.
Our frequency-aware decomposition enables progressive Level of Detail (LOD) rendering, where low-frequency Gaussians capture coarse geometry and appearance, and higher levels progressively refine the image with finer details.
By grouping Gaussians according to Laplacian pyramid subbands, we isolate and visualize distinct frequency components of a scene, revealing how each level contributes to the final image structure.
Our model supports real-time foveated rendering by concentrating high-frequency details in the user’s gaze region, reducing rendering cost in the periphery without compromising perceptual quality.
Frequency-aware decomposition allows selective object editing based on text prompts by preserving only relevant high-frequency Gaussians, enabling multi-view-consistent object highlighting or suppression.
Manipulating frequency-specific Gaussian groups enables stylized effects such as X-ray or painterly rendering, while preserving 3D consistency and coherence across viewpoints.
Operating on only the low-frequency Gaussians enables faster geometric queries and interactions, such as collision detection or spatial search, while maintaining structural fidelity.
