Frequency-Aware Gaussian Splatting Decomposition (2503.21226v1)

Abstract: 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 code will be made publicly available.

• The paper introduces a frequency-aware decomposition framework for 3D Gaussian Splatting, organizing Gaussians by subbands in Laplacian Pyramids of input images.
• The frequency-aware framework improves interpretability and enables applications like 3D editing, stylization, and adaptive rendering.
• The method enables selective rendering of frequency levels, providing controllable fidelity beneficial for real-time applications like VR or streaming.

Frequency-Aware Gaussian Splatting Decomposition: A Technical Overview

The paper "Frequency-Aware Gaussian Splatting Decomposition" introduces an innovative approach to 3D view synthesis by implementing a frequency-aware decomposition within the framework of 3D Gaussian Splatting (3D-GS). Authored by Yishai Lavi, Leo Segre, and Shai Avidan from Tel Aviv University, the work addresses a notable limitation in 3D-GS, namely its lack of frequency interpretability, which poses challenges in distinguishing between low-frequency structures and high-frequency details.

Core Contributions

The paper presents a novel method that organizes 3D Gaussians according to subbands in the Laplacian Pyramids of input images. This frequency-decomposed framework not only enhances interpretability but also enables innovative applications such as frequency-aware 3D editing and stylization, adaptive level-of-detail (LOD) rendering, streaming, fast geometry interaction, and foveated rendering.

The authors employ dedicated regularization to ensure coherence within each frequency subband, thereby achieving well-separated frequency components. Additionally, the color values extend to both positive and negative ranges, allowing high-frequency layers to add or subtract residual details—a mechanism analogous to residuals in Laplacian pyramids. To refine the optimization process, a progressive training scheme is utilized, where details are refined sequentially from coarse to fine.

Methodological Insights

The paper elaborates on the integration of frequency sub-bands into 3D-GS. It involves grouping Gaussians based on their contribution across different frequency bands of the Laplacian pyramid of input images. This grouping facilitates a hierarchy within 3D-GS allowing selective rendering—akin to classical 2D image pyramids but in 3D. Rendering various subsets of frequency levels offers controllable fidelity tuning, which is particularly advantageous for real-time applications requiring differing levels of detail.

Through extensive experiments, the paper claims improved control and flexibility for applications in scene editing and interactive rendering. By addressing frequency separation and rendering stability, this framework promises enhanced interpretability and pragmatic benefits in real-time systems.

Implications and Speculations

The implications of this research are manifold. On a practical level, the method could revolutionize applications requiring dynamic rendering adjustments, such as virtual reality environments and remote streaming applications. The ability to control fidelity across frequency bands offers distinct advantages in terms of rendering efficiency and visual quality.

From a theoretical standpoint, this work advances the understanding of hierarchical representations within 3D rendering. The structured separation of frequency components may inspire further research in the domain of real-time rendering, potentially facilitating developments in efficient scene manipulation, rendering technologies, and adaptive visualization.

Future work could explore extensions and optimizations that further increase efficiency or integrate machine learning elements to assist in faster frequency band identification and grouping. As AI continues to evolve, techniques similar to those presented in this paper could enable automatic scene rendering that dynamically adapts to user preferences or device capabilities.

In conclusion, this paper introduces a significant enhancement to 3D Gaussian Splatting by introducing frequency-aware decomposition—an advancement that promises to broaden the horizon for real-time 3D rendering applications.