Evaluating Light Probe Estimation Techniques for Mobile Augmented Reality

Realistic lighting approaches typically rely on physically-based rendering which in turn often makes use of image-based lighting. Enabling these techniques in augmented reality on mobile devices requires unique approaches to estimate light probes, given the limited camera and sensor data available. In this paper, we evaluate different time-dependent and time-independent techniques for light probe estimation in augmented reality applications that try to predict the environment lighting using single images or video streams in combination with inpainting techniques. We simulate real-world applications using an evaluation framework where a simulated mobile device captures camera streams from different scenarios following a pre-defined path. The resulting camera streams are fed to a total of six estimation techniques in order to create light probes which are then used to render virtual objects while applying various materials. By comparing the rendered images as well as the light probe estimations, we perform a quantitative evaluation. We show how approaches that are able to process continuous video streams can provide more plausible results in cases where sufficient camera movement is present. Additionally, we investigate the visual impression of different types of materials showing that rough surfaces with distinct colors are less likely to produce divergent estimation results.