Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing (2311.16043v2)

Abstract: In this paper, we present a novel differentiable point-based rendering framework to achieve photo-realistic relighting. To make the reconstructed scene relightable, we enhance vanilla 3D Gaussians by associating extra properties, including normal vectors, BRDF parameters, and incident lighting from various directions. From a collection of multi-view images, the 3D scene is optimized through 3D Gaussian Splatting while BRDF and lighting are decomposed by physically based differentiable rendering. To produce plausible shadow effects in photo-realistic relighting, we introduce an innovative point-based ray tracing with the bounding volume hierarchies for efficient visibility pre-computation. Extensive experiments demonstrate our improved BRDF estimation, novel view synthesis and relighting results compared to state-of-the-art approaches. The proposed framework showcases the potential to revolutionize the mesh-based graphics pipeline with a point-based pipeline enabling editing, tracing, and relighting.

• The paper introduces a differentiable rendering framework that assigns BRDF properties to 3D Gaussian points for robust point cloud relighting.
• It leverages a novel point-based ray tracing method with a bounding volume hierarchy to enable realistic shadow rendering and efficient visibility determination.
• Experimental results show superior BRDF estimation and novel view quality at 120 FPS, outperforming traditional mesh-based techniques.

Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing

The paper introduces a novel differentiable rendering framework named "Relightable 3D Gaussian" for real-time relighting, ray tracing, and editing of 3D point clouds derived from multi-view images. This work represents a considerable advancement in the seamless integration of material and lighting decomposition into point-based rendering, leveraging 3D Gaussian points as primitives. The authors systematically enhance the traditional 3D Gaussian Splatting (3DGS) approach by introducing additional attributes—such as normal direction and Bidirectional Reflectance Distribution Function (BRDF) parameters—to each Gaussian point, thus facilitating robust lighting estimation and rendering capabilities.

Key Contributions

1. Material and Lighting Decomposition: The framework assigns each 3D Gaussian point with additional BRDF properties and incident light information. By categorizing incident lights into global and local components, it promotes a meticulous estimation that accommodates complex lighting scenarios.
2. Point-Based Ray Tracing: A novel point-based ray-tracing method, built upon a bounding volume hierarchy, enables efficient visibility determination and realistic shadow rendering. This contribution addresses a significant limitation in traditional point-based representations, which often struggle with achieving realistic renderings due to inadequate ray tracing capabilities.
3. Efficient Rendering Pipeline: The framework consolidates relightable and editable rendering solely based on point clouds, significantly streamlining processes when compared to mesh-based graphics pipelines.

Experimental Results

Extensive experiments across synthetic and real-world datasets demonstrate notable improvements in BRDF estimation and novel view rendering quality, evidenced by strong performance in standard metrics like PSNR and SSIM. Remarkably, the framework achieves competitive results with state-of-the-art methods while maintaining real-time rendering efficiency at 120 FPS.

Theoretical and Practical Implications

By advancing point-based rendering methodologies with integrated BRDF decomposition and ray tracing, this work not only enhances the theoretical understanding of light-material interaction in 3D graphics but also poses implications for practical applications in real-time systems where mesh complexity and rendering speed are crucial constraints. The explicit parameterization of visibility and lighting supports substantial adaptability in diverse environments, underscored by the project's application to complex scenes with varying object compositions.

Speculation on Future Developments

This work opens the door for further explorations into increased geometric complexity handling and finer detail in lighting conditions, potentially involving synergistic integration with learning-based approaches for intrinsic scene understanding. Future endeavors could capitalize on multi-view stereo cues more integrally or explore hybrid methods combining mesh and point cloud merits for enhanced scalability and precision.

In conclusion, the presented "Relightable 3D Gaussian" framework serves as a comprehensive and efficient solution for real-time, high-quality 3D scene rendering. It redefines current methodologies by addressing core limitations in relighting and editing, positioning itself as a valuable contribution to the domain of computer graphics and vision.