Abstract Real‐time path tracing for global illumination has recently become feasible on high‐performance desktop GPUs, but achieving similar performance on mobile platforms remains a significant challenge due to computational limitations. As mobile devices begin to integrate ray tracing capabilities, new methods are required to bridge the performance gap and enable advanced rendering techniques on constrained hardware. In this paper, we present Mobile Radiance Caching (MobileRC), an online trainable radiance caching approach based on a plenoxel representation, designed to accelerate path tracing on mobile devices. Unlike neural network‐based radiance caching methods, which rely on matrix multiplication accelerators unavailable on current mobile GPUs, MobileRC uses a voxel‐based representation where each voxel stores spherical harmonic coefficients to represent angular dependencies, making it more suitable for mobile hardware. Specifically, we exploit the localized interaction between learnable plenoxel weights and training samples, designing a mobile‐friendly training method. While our approach incurs a mild loss in cache quality compared to neural methods optimized for high‐end GPUs, it significantly improves image quality while reducing rendering time, achieving interactive frame rates for full HD images in room‐sized scenes on mobile hardware.

New augmented-reality near-eye displays provide capabilities for enriching real-world visual experiences with digital content. Most current research focuses on improving both hardware and software to provide digital content that seamlessly blends with the real world. This is believed to not only contribute to the visual experience but also increase human task performance. In this work, we take a step further and ask the question of whether the capabilities of current and future display designs combined with efficient perception-inspired content optimizations can be used to improve human task performance beyond the human capabilities in the natural world. Based on an in-depth analysis of previous literature, we hypothesize here that such enhancements can be achieved when the human visual system is provided with content that optimizes the oculomotor responses. To further investigate possible gains, we present a series of perceptual experiments that built upon this idea. More specifically, we focus on speeding up accommodation response, which significantly contributes to the eye-adaptation when a new stimulus is shown. Through our experiments, we demonstrate that such speedups canbe achieved, and more importantly, they can lead to significant improvements in human task performance. While not all of our results give definite answers, we believe that they reveal plentiful opportunities for further enhancing the human experience and task performance when using new augmented-reality displays.

Multi-focal plane and multi-layered light-field displays are promising solutions for addressing all visual cues observed in the real world. Unfortunately, these devices usually require expensive optimizations to compute a suitable decomposition of the input light field or focal stack to drive individual display layers. Although these methods provide near-correct image reconstruction, a significant computational cost prevents real-time applications. A simple alternative is a linear blending strategy which decomposes a single 2D image using depth information. This method provides real-time performance, but it generates inaccurate results at occlusion boundaries and on glossy surfaces. This paper proposes a perception-based hybrid decomposition technique which combines the advantages of the above strategies and achieves both real-time performance and high-fidelity results. The fundamental idea is to apply expensive optimizations only in regions where it is perceptually superior, e.g., depth discontinuities at the fovea, and fall back to less costly linear blending otherwise. We present a complete, perception-informed analysis and model that locally determine which of the two strategies should be applied. The prediction is later utilized by our new synthesis method which performs the image decomposition. The results are analyzed and validated in user experiments on a custom multi-plane display.