Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can model noise, MB and DoF to make the virtual content visually consistent with the video. Existing methods for this typically suffer two main limitations. First, they require a camera calibration step to relate a known calibration target to the specific cameras response. Second, existing work require methods that can be (differentiably) tuned to the calibration, such as slow and specialized neural networks. We propose a method which estimates parameters for noise, MB and DoF instantly, which allows using off-the-shelf real-time simulation methods from e.g., a game engine in compositing augmented content. Our main idea is to unlock both features by showing how to use modern computer vision methods that can remove noise, MB and DoF from the video stream, essentially providing self-calibration. This allows to auto-tune any black-box real-time nose+MB-DoF method to deliver fast and high-fidelity augmentation consistency.

Recent rendering advances dramatically reduce the cost of global illumination. But even with hardware acceleration, complex light paths with multiple glossy interactions are still expensive; our new algorithm stores these paths in precomputed light probes and reprojects them at runtime to provide interactivity. Combined with traditional light maps for diffuse lighting our approach interactively renders all light paths in static scenes with opaque objects. Naively reprojecting probes with glossy lighting is memory-intensive, requires efficient access to the correctly reflected radiance, and exhibits problems at occlusion boundaries in glossy reflections. Our solution addresses all these issues. To minimize memory, we introduce an adaptive light probe parameterization that allocates increased resolution for shinier surfaces and regions of higher geometric complexity. To efficiently sample glossy paths, our novel gathering algorithm reprojects probe texels in a view-dependent manner using efficient reflection estimation and a fast rasterization-based search. Naive probe reprojection often sharpens glossy reflections at occlusion boundaries, due to changes in parallax. To avoid this, we split the convolution induced by the BRDF into two steps: we precompute probes using a lower material roughness and apply an adaptive bilateral filter at runtime to reproduce the original surface roughness. Combining these elements, our algorithm interactively renders complex scenes while fitting in the memory, bandwidth, and computation constraints of current hardware.

Mixed reality mobile platforms attempt to co-locate virtual scenes with physical environments, towards creating immersive user experiences. However, to create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic lighting that matches the physical environment. To this end, we design GLEAM, a framework that provides robust illumination estimation in real-time by integrating physical light-probe estimation with current mobile AR systems. GLEAM visually observes reflective objects to compose a realistic estimation of physical lighting. Optionally, GLEAM can network multiple devices to sense illumination from different viewpoints and compose a richer estimation to enhance realism and fidelity. Using GLEAM, AR developers gain the freedom to use a wide range of materials, which is currently limited by the unrealistic appearance of materials that need accurate illumination, such as liquids, glass, and smooth metals. Our controlled environment user studies across 30 participants reveal the effectiveness of GLEAM in providing robust and adaptive illumination estimation over commercial status quo solutions, such as pre-baked directional lighting and ARKit 2.0 illumination estimation. Our benchmarks reveal the need for situation driven tradeoffs to optimize for quality factors in situations requiring freshness over quality and vice-versa. Optimizing for different quality factors in different situations, GLEAM can update scene illumination as fast as 30ms by sacrificing richness and fidelity in highly dynamic scenes, or prioritize quality by allowing an update interval as high as 400ms in scenes that require high-fidelity estimation.

Mixed reality mobile platforms attempt to co-locate virtual scenes with physical environments, towards creating immersive user experiences. However, to create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic lighting that matches the physical environment. To this end, we design GLEAM, a framework that provides robust illumination estimation in real-time by integrating physical light-probe estimation with current mobile AR systems. We present a demo implementation of GLEAM by means of an AR application that estimates environmental illumination and renders the scene with real-time illumination updates. We demonstrate the efficacy of GLEAM's estimation against a current commercial status quo solution, Apple's ARKit, with the same application.