Hardware-Rasterized Ray-Based Gaussian Splatting (2503.18682v2)

Abstract: We present a novel, hardware rasterized rendering approach for ray-based 3D Gaussian Splatting (RayGS), obtaining both fast and high-quality results for novel view synthesis. Our work contains a mathematically rigorous and geometrically intuitive derivation about how to efficiently estimate all relevant quantities for rendering RayGS models, structured with respect to standard hardware rasterization shaders. Our solution is the first enabling rendering RayGS models at sufficiently high frame rates to support quality-sensitive applications like Virtual and Mixed Reality. Our second contribution enables alias-free rendering for RayGS, by addressing MIP-related issues arising when rendering diverging scales during training and testing. We demonstrate significant performance gains, across different benchmark scenes, while retaining state-of-the-art appearance quality of RayGS.

Overview of Hardware-Rasterized Ray-Based Gaussian Splatting for Novel View Synthesis

The paper Hardware-Rasterized Ray-Based Gaussian Splatting presents an innovative approach to rendering ray-based 3D Gaussian splatting (RayGS), aimed at achieving both high-quality and expedient solutions for novel view synthesis. Addressing practical challenges in virtual and mixed reality applications, this research contributes significantly by introducing a technique that leverages hardware rasterization to enhance the rendering speed while preserving the quality associated with RayGS models.

Context and Motivation

With recent advancements in image-based reconstruction methods like Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), photorealistic rendering for novel view synthesis has seen substantial improvements. These techniques enable the capture of intricate real-world scene details crucial for applications in virtual and mixed reality, offering interpretation, flexibility, and efficiency that are vital for real-time rendering. Although 3DGS has surmounted NeRFs in speed, the need for visual quality at acceptable frame rates persists, especially in VR applications where rendering artifacts can considerably affect the immersive user experience.

Key Contributions

1. Mathematically Rigorous Derivation: This research presents a detailed and geometrically intuitive derivation aimed at efficiently estimating the quantities necessary for rendering RayGS models. Importantly, this work restructures these estimates to be compatible with standard hardware rasterization shaders, thus facilitating fast and high-quality rendering.
2. Hardware-Rasterized Solution: By employing hardware-rasterization pipelines, the approach successfully renders RayGS primitives using traditional graphics pipeline components, a methodology reminiscent of existing frameworks used for standard 3DGS models. The paper elucidates the derivation of minimal enclosing quads in 3D space, offering a rendering speed comparable to hardware-rasterized 3DGS while elevating visual fidelity.
3. MIP Formulation: The research addresses MIP-related issues within RayGS, enhancing alias-free image rendering at varying test and training scales. This formulation pans out via a normalized ray, integrating marginalization of the primitive's Gaussian distribution and subsequently approximating the integral over pixel areas for accurate and efficient opacity computation sitewide.

Experimental Validation

The proposed VKRayGS renderer, operating on the Vulkan platform, demonstrates notable performance enhancements within benchmark datasets, indicating an average of approximately 40 times faster rendering while retaining high-quality appearance. The qualitative comparisons and speed metrics underscore the substantial practical benefits of integrating hardware rasterization in rendering RayGS models.

Theoretical and Practical Implications

Theoretically, the paper contributes a framework for understanding how efficient estimation within hardware environments can be structured for sophisticated rendering tasks. Practically, it significantly impacts VR applications by enabling real-time rendering without compromising the quality that RayGS provides over standard 3DGS. This advancement broadens the accessibility of high-fidelity rendering even in computationally constrained environments, making it feasible for consumer-grade hardware.

Speculative Future Directions

As future directions, the incorporation of hardware-supported differentiation during the training phase presents a compelling research opportunity. Further exploration into refined rendering logic optimization could potentially amplify efficiencies beyond what current rasterization strategies achieve. Moreover, extending this framework across varied rendering environments beyond Vulkan could leverage the generalizability of the established theoretical insights for diverse applications in AI and computational visualization.