Online Photon Guiding with 3D Gaussians for Caustics Rendering (2403.03641v2)

Abstract: In production rendering systems, caustics are typically rendered via photon mapping and gathering, a process often hindered by insufficient photon density. In this paper, we propose a novel photon guiding method to improve the photon density and overall quality for caustic rendering. The key insight of our approach is the application of a global 3D Gaussian mixture model, used in conjunction with an adaptive light sampler. This combination effectively guides photon emission in expansive 3D scenes with multiple light sources. By employing a global 3D Gaussian mixture, our method precisely models the distribution of the points of interest. To sample emission directions from the distribution at any observation point, we introduce a novel directional transform of the 3D Gaussian, which ensures accurate photon emission guiding. Furthermore, our method integrates a global light cluster tree, which models the contribution distribution of light sources to the image, facilitating effective light source selection. We conduct experiments demonstrating that our approach robustly outperforms existing photon guiding techniques across a variety of scenarios, significantly advancing the quality of caustic rendering.

• The paper introduces a novel photon guiding approach using 3D Gaussian mixtures to accurately model photon emission distributions for superior caustics rendering.
• It integrates a unique directional transform and adaptive light sampling, significantly reducing mean square error and enhancing image quality.
• The method improves efficiency in production rendering systems by optimizing light source contributions and paving the way for future path guiding advancements.

Online Photon Guiding with 3D Gaussians for Caustics Rendering

The paper presents an advanced photon guiding technique specifically designed for high-quality caustic rendering in production rendering systems. The proposed method hinges on the innovative application of 3D Gaussian mixtures to improve photon density and image quality. This strategy addresses inherent challenges in photon mapping by mitigating the often insufficient photon density in expansive scenes, particularly those with diverse light sources.

The authors introduce a novel photon guiding approach that utilizes 3D Gaussian mixtures coupled with an adaptive light sampler. This combination ensures efficient photon emission guidance across complex 3D environments. The 3D Gaussian mixture, applied individually to each light source, models the spatial distribution of interest for photon emissions with greater precision. Crucially, the paper details a unique directional transform of the 3D Gaussian, developed to facilitate accurate photon emission, which enhances the photon density where caustics are visually prominent.

To further bolster the rendering efficacy, the researchers propose an innovative scene-geometry-based initialization technique, which substantially accelerates optimization processes. The incorporation of a global light cluster tree models light source contributions more effectively, optimizing light source selection and improving rendering performance.

Quantitative experiments reveal that this method consistently surpasses existing photon guiding techniques across various scenarios, demonstrating its robust performance. The framework significantly advances caustic rendering capabilities by achieving higher photon densities in visible regions with minimal computational overhead.

Numerical Results and Implications

The experiments conducted highlight the method’s superior performance. When compared with traditional methods such as 2D histograms and 3D von Mises-Fisher distributions, the new approach using 3D Gaussian mixtures achieves notable improvements in mean square error (MSE) and structural similarity index measure (SSIM), showcasing its ability to produce images with fewer errors and superior visual quality. For instance, across different test scenes, the proposed method often records MSE values an order of magnitude lower than those from competing approaches.

The implications of this research are twofold. Practically, the enhanced photon guiding framework could be directly integrated into production-level rendering engines, leading to more efficient and visually accurate renderings, especially in scenes with complex caustics. Theoretically, the introduction of 3D Gaussian mixtures and the novel directional transform as a model for photon emission paves the way for future exploration into path guiding and related graphical computations.

Future Directions

Future developments envisioned by the authors include extending the method to support cosine-weighted guiding for emission directions and exploring hybrid techniques that combine path guiding for indirect lighting. Another potential avenue could be further refining the algorithm to accommodate complex multi-lobe BSDF materials, which are prevalent in photorealistic rendering tasks. Enhanced adaptability to such models could amplify the applicability of this technique across more diverse rendering scenarios.

The paper represents a solid advancement in the field of computer graphics, offering a robust solution for one of the most intricate aspects of realistic rendering—caustics—while also indicating fertile ground for continuing research and development.