Accelerating Stereo Rendering via Image Reprojection and Spatio-Temporal Supersampling

Abstract

Achieving immersive virtual reality (VR) experiences typically requires extensive computational resources to ensure high-definition visuals, high frame rates, and low latency in stereoscopic rendering. This challenge is particularly pronounced for lower-tier and standalone VR devices with limited processing power. To accelerate rendering, existing supersampling and image reprojection techniques have shown significant potential, yet to date, no previous work has explored their combination to minimize stereo rendering overhead. In this paper, we introduce a lightweight supersampling framework that integrates image projection with spatio-temporal supersampling to accelerate stereo rendering. Our approach effectively leverages the temporal and spatial redundancies inherent in stereo videos, enabling rapid image generation for unshaded viewpoints and providing resolution-enhanced and anti-aliased images for binocular viewpoints. We first blend a rendered low-resolution (LR) frame with accumulated temporal samples to construct an high-resolution (HR) frame. This HR frame is then reprojected to the other viewpoint to directly synthesize a new image. To address disocclusions in reprojected images, we utilize accumulated history data and low-pass filtering for filling, ensuring high-quality results with minimal delay. Extensive evaluations on both the PC and the standalone device confirm that our framework requires short runtime to generate high-fidelity images, making it an effective solution for stereo rendering across various VR platforms.