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.

Contrast detection thresholds were measured for the eccentricities from 0° to 27° and a range of stimuli frequencies from 0.125cpd to 16cpd. The measurements were motivated by the need to collect visual performance data for the gaze-contingent rendering system. For this application, the mixed chromatic and achromatic stimuli are even more important than purely chromatic cases. Therefore, the detection of sine-gratings with Gaussian patches was measured for four mixed chromatic/achromatic with a varying share of the achromatic components. To verify that our experimental setup generates the results consistent with the previous work, we also measured the contrast thresholds for achromatic (black to white) stimulus. Five observers participated in the experiments and they individually determined the detection threshold for each stimulus using the QUEST method. The results plotted as the contrast sensitivity function (CSF) follow the state-of-the-art CSF models. However, we report lower sensitivity to contrast for achromatic stimuli caused by the small size of the stimulus. The color directions closer to the chromatic green-to-red axis show higher contrast sensitivity in comparison to achromatic stimuli, while for the yellow-to-blue axis the sensitivity is lower. The higher achromatic component in the mixed stimuli approaches contrast sensitivity to the achromatic CSF.

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.