Hybrelighter: Combining Deep Anisotropic Diffusion and Scene Reconstruction for On-device Real-time Relighting in Mixed Reality

Abstract

Mixed Reality scene relighting, where virtual changes to lighting conditions realistically interact with physical objects, producing authentic illumination and shadows, can be used in a variety of applications. One such application in real estate could be visualizing a room at different times of day and placing virtual light fixtures. Existing deep learning-based relighting techniques typically exceed the real-time performance capabilities of current MR devices. On the other hand, scene understanding methods, such as on-device scene reconstruction, often yield inaccurate results due to scanning limitations, in turn affecting relighting quality. Finally, simpler 2D image filter-based approaches cannot represent complex geometry and shadows. We introduce a novel method to integrate image segmentation, with lighting propagation via anisotropic diffusion on top of basic scene understanding, and the computational simplicity of filter-based techniques. Our approach corrects on-device scanning inaccuracies, delivering visually appealing and accurate relighting effects in real-time on edge devices, achieving speeds as high as 100 fps. We show a direct comparison between our method and the industry standard, and present a practical demonstration of our method in the aforementioned real estate example.