SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors (2411.18966v1)

Abstract: Gaussian Splattings demonstrate impressive results in multi-view reconstruction based on Gaussian explicit representations. However, the current Gaussian primitives only have a single view-dependent color and an opacity to represent the appearance and geometry of the scene, resulting in a non-compact representation. In this paper, we introduce a new method called SuperGaussians that utilizes spatially varying colors and opacity in a single Gaussian primitive to improve its representation ability. We have implemented bilinear interpolation, movable kernels, and even tiny neural networks as spatially varying functions. Quantitative and qualitative experimental results demonstrate that all three functions outperform the baseline, with the best movable kernels achieving superior novel view synthesis performance on multiple datasets, highlighting the strong potential of spatially varying functions.

• The paper introduces a novel method that enhances Gaussian splatting by incorporating spatially varying colors and opacities for improved scene fidelity.
• It details three approaches—bilinear interpolation, movable kernels, and tiny neural networks—each enabling dynamic color and opacity assignment within each primitive.
• Experiments on benchmark datasets demonstrate superior PSNR, SSIM, and LPIPS scores, confirming higher rendering quality and computational efficiency.

Overview of the SuperGaussians Approach

The academic paper entitled "SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors" introduces a novel method to improve the representation capacity of Gaussian splatting for novel view synthesis (NVS) in computer graphics and vision tasks. The authors propose the use of a method called SuperGaussians to address the limitations of existing Gaussian splatting methods, which traditionally employ primitives with uniform view-dependent color and opacity. SuperGaussians enhance the expressiveness of Gaussian primitives by incorporating spatially varying attributes within each primitive, thereby achieving high fidelity in scene representation without an increase in computational resources.

Methodological Contributions

The core innovation presented in this paper involves spatially varying colors and opacity within a single Gaussian primitive. The authors explore three spatially varying functions to improve Gaussian representation:

1. Bilinear Interpolation: This method divides each Gaussian surface into four quadrants, assigning distinct learnable color and opacity values to each. Bilinear interpolation is employed to calculate the color and opacity at any pixel location across the primitive, thus enhancing expressive power, albeit with potential gradient vanishing issues in uniform regions.
2. Movable Kernels: Offering a higher degree of expressiveness, this approach utilizes movable kernels which act as dynamic points of influence to determine color and opacity. Each kernel can move across the Gaussian surfel during training, giving rise to a more flexible representation that can closely follow complex surface features.
3. Tiny Neural Networks: By attaching a small neural network to each Gaussian surfel, the system can compute color and opacity values at any intersection point dynamically. Despite the increase in parameters and computational overhead, this method achieves superior representational capacity, particularly under constraints with fewer Gaussians.

These functions offer marked improvements over traditional Gaussian splatting methods by allowing single Gaussian primitives to adapt to the spatially varying textures and opacities present in complex scenes.

Experimental Results and Numerical Evidence

The paper's experimental validations on multi-view datasets, including Synthetic Blender, Mip-NeRF360, Tanks and Temples, and DTU, indicate that SuperGaussians outperform existing Gaussian splatting methods, such as 2DGS and 3DGS, especially in scenarios with limited numbers of Gaussian primitives. The authors highlight that the movable kernels method provides the most marked improvement in rendering quality, surpassing conventional benchmarks and rivaling state-of-the-art methods in novel view synthesis tasks.

Quantitative results are demonstrated on metrics like PSNR, SSIM, and LPIPS, showing improvements over both existing baselines and other spatially varying function implementations. Furthermore, the ability to maintain high content fidelity with fewer Gaussian primitives suggests enhanced computational efficiency and potential scaling advantages.

Implications and Future Developments

The introduction of SuperGaussians is significant for the field of computer graphics and NVS, offering a pathway to more compact and effective scene representations. The method aligns well with tasks requiring efficient rendering and reconstruction, such as AR/VR applications, robotic vision systems, and autonomous driving environments.

Future research avenues proposed by the authors include optimizing computation time and exploring more sophisticated spatially varying functions that could yield further enhancements in scene representation. Bridging the existing gap with implicit NeRF-like methods through spatially varying explicit representations could position SuperGaussians as a pivotal approach in balancing representation power with computational efficiency.

By affirming the potent capabilities of spatially varying functions within Gaussian frameworks, the paper paves the way for continued exploration into high-fidelity, efficient scene synthesis methods.