GS^3: Efficient Relighting with Triple Gaussian Splatting (2410.11419v1)

Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance. Our code and data are publicly available at https://GSrelight.github.io/.

• The paper presents a triple Gaussian splatting method that efficiently synthesizes real-time lighting variations using spatial and angular Gaussians.
• It employs a differentiable reflectance function combined with deferred shading to accurately capture shadows and complex material properties.
• It achieves rendering speeds of up to 90 fps on commodity GPUs while maintaining high-quality reconstructions across diverse scenarios.

Overview of "GS: Efficient Relighting with Triple Gaussian Splatting"

The paper "GS: Efficient Relighting with Triple Gaussian Splatting" presents an advanced method for synthesizing high-quality, real-time lighting and view transformations using a Gaussian-based representation. This approach leverages spatial and angular Gaussians along with a triple splatting process, marking a significant step in the manipulation of complex 3D scenes under variable lighting and viewing conditions.

Methodology

The authors introduce a novel representation that utilizes spatial and angular Gaussians to model complex appearances. Key components include:

• Triple Gaussian Splatting: This technique splats spatial Gaussians in three phases for shading, shadowing, and compensating additional light effects, ensuring efficient computation.
• Differentiable Reflectance Function: A blend of Lambertian reflectance and angular Gaussians is used, enabling the modeling of anisotropic reflections effectively.
• Deferred Shading: Shadows are generated by splatting Gaussians toward a light source and refining these values using a small MLP to enhance overall shadow quality.

This method is demonstrated through various samples, showing its adaptability to complex geometries and appearances, using different forms of input data, ranging from synthetic to real-world photographs.

Results

The representation achieves a training time of 40-70 minutes and a rendering speed of 90 frames per second on a single commodity GPU. The paper reports favorable comparisons with state-of-the-art techniques, maintaining high performance and quality across numerous trials:

• High-Quality Reconstruction: The method successfully reconstructs complex materials like translucent and anisotropic surfaces, demonstrating robustness and accuracy.
• Quantitative Metrics: Numerical results such as PSNR and SSIM across different test cases align closely with or surpass previous methods, highlighting the technique's efficiency and effectiveness.

Implications

The proposed method advances real-time rendering capabilities, reducing computational overhead while maintaining output quality. The triple Gaussian splatting approach underscores potential applications in industries requiring high-fidelity digital replicas under various lighting conditions, such as visual effects, cultural heritage preservation, and e-commerce.

Theoretical and Practical Implications

• Theoretically, this could inspire further research into optimizing 3D scene representations, particularly in the context of differentiable rendering and implicit modeling.
• Practically, the method's efficiency on commodity hardware opens avenues for deploying advanced lighting techniques in consumer applications, broadening accessibility to sophisticated graphical tools.

Future Directions

The authors identify several areas for future exploration:

• Addressing limitations concerning transparent materials or extremely high-frequency appearances.
• Enhancing the spatial granularity of Gaussians to better capture fine details.
• Developing methods to optimize acquisition conditions, potentially reducing input data requirements while improving quality.

In conclusion, this paper provides substantial contributions to the field of computer graphics, offering a balanced approach between computational efficiency and visual fidelity, setting a foundation for further exploration in dynamic rendering processes. The public availability of their code and data will likely catalyze subsequent innovations and practical implementations.