Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
In the field of neural rendering, Neural Radiance Fields (NeRFs) have exhibited significant capabilities in reconstructing photorealistic 3D scenes using a collection of 2D images. A notable barrier to their widespread utilization, however, is the computational inefficiency originating from ray-wise volumetric rendering. As an alternative, 3D Gaussian splatting (3DGS) has gained traction given its promise in offering fast rendering speeds paired with commendable image quality through the usage of a 3D Gaussian-based representation. Despite its advantages, a critical drawback of 3DGS is its voluminous memory and storage demands, contingent on maintaining a high number of Gaussians for image fidelity.
The paper proposes methodologies to mitigate these issues, focusing on two primary goals: the reduction of Gaussian points without performance degradation, and the compression of Gaussian attributes (view-dependent color, covariance). An innovative learnable mask strategy is introduced, which effectively reduces the number of Gaussians while preserving high performance. Additionally, a compact view-dependent color representation utilizing a grid-based neural field is employed, diverging from the traditional reliance on spherical harmonics. To further compact the model, the paper also proposes learning codebooks via residual vector quantization (R-VQ) to represent the geometric and temporal attributes compactly.
Key Contributions
- Learnable Mask Strategy: The paper introduces an end-to-end optimization framework incorporating a learnable mask applied to Gaussian attributes, reducing redundancy by eliminating Gaussians with minimal performance impact.
- Compact View-Dependent Color Representation: Shifting from spherical harmonics to a grid-based neural field helps to more efficiently represent view-dependent colors.
- Residual Vector Quantization (R-VQ): This technique is applied to encode geometrical and temporal attributes, capitalizing on the limited variability among Gaussians.
Numerical Results and Performance
The developed methodology was rigorously tested on various datasets, including Mip-NeRF 360, Tanks and Temples, Deep Blending, and NeRF-Synthetic. It consistently demonstrated over 25x reduced storage for static scenes while accelerating rendering speeds compared to the original 3DGS. For dynamic scenes, the research achieved more than 12x storage efficiency and retained high-quality reconstructions.
- Static Scenes: On datasets like Mip-NeRF 360 and Tanks and Temples, the proposed method closely matched the original 3DGS in rendering quality but with drastically reduced storage demands—down from multiple gigabytes to mere tens of megabytes. Additionally, the rendering performance saw substantial improvements.
- Dynamic Scenes: Using datasets like DyNeRF and Technicolor, the approach achieved significant compression while maintaining or slightly reducing the computational overhead compared to state-of-the-art methods such as STG.
Implications and Future Directions
The demonstrated compression techniques for 3D Gaussian representations have broad implications for neural rendering and various interactive 3D applications where both storage and computational efficiency are crucial. This work lays a foundation for more practical and scalable neural rendering systems.
Theoretical advancements accounted for in this research, such as the novel learnable masking and efficient use of R-VQ, provide a substantial leap towards more compact neural representations that do not compromise on the rendering quality. Practically, this work paves the way for more enablement of real-time rendering on resource-constrained devices, thereby broadening the applications in fields such as augmented reality (AR), virtual reality (VR), and even mobile computing.
Future research could develop further on optimizing the grid-based neural field or explore additional masking strategies reflecting different properties or contextual importance of Gaussians. Another promising avenue would be integrating these techniques with more advanced hardware accelerations tailored to neural rendering tasks, enabling even broader accessibility and efficiency.
Overall, this paper contributes significantly to the ongoing advancements in neural rendering, presenting methodologies that bridge the gap between quality and computational efficiency both in static and dynamic 3D scenes.