VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points

TL;DR

This paper introduces a hybrid foveated rendering method for VR that combines Gaussian splatting for peripheral vision and neural points for the fovea, achieving high-quality, real-time scene rendering optimized for human visual perception.

Contribution

The paper presents a novel foveated rendering approach that integrates Gaussian splatting and neural points, improving VR rendering performance and visual quality by leveraging human visual system insights.

Findings

Enhanced sharpness and detail over standard VR 3DGS

Participants preferred the foveated rendering method

Achieved real-time performance suitable for VR

Abstract

Recent advances in novel view synthesis have demonstrated impressive results in fast photorealistic scene rendering through differentiable point rendering, either via Gaussian Splatting (3DGS) [Kerbl and Kopanas et al. 2023] or neural point rendering [Aliev et al. 2020]. Unfortunately, these directions require either a large number of small Gaussians or expensive per-pixel post-processing for reconstructing fine details, which negatively impacts rendering performance. To meet the high performance demands of virtual reality (VR) systems, primitive or pixel counts therefore must be kept low, affecting visual quality. In this paper, we propose a novel hybrid approach based on foveated rendering as a promising solution that combines the strengths of both point rendering directions regarding performance sweet spots. Analyzing the compatibility with the human visual system, we find that using a low-detailed, few primitive smooth Gaussian representation for the periphery is cheap to compute and meets the perceptual demands of peripheral vision. For the fovea only, we use neural points with a convolutional neural network for the small pixel footprint, which provides sharp, detailed output within the rendering budget. This combination also allows for synergistic method accelerations with point occlusion culling and reducing the demands on the neural network. Our evaluation confirms that our approach increases sharpness and details compared to a standard VR-ready 3DGS configuration, and participants of a user study overwhelmingly preferred our method. Our system meets the necessary performance requirements for real-time VR interactions, ultimately enhancing the user's immersive experience. The project page can be found at: https://lfranke.github.io/vr_splatting