Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality
This work addresses the problem of photorealistic rendering in emerging front-facing AR applications for users, representing an incremental improvement over existing methods.
The paper tackles the challenge of generating high-quality environment maps for front-facing mobile AR applications, such as virtual try-on, by leveraging dual-camera streaming to combine multi-view lighting reconstruction and parametric directional lighting estimation, resulting in improved rendering quality compared to a commercial solution.
Lighting understanding plays an important role in virtual object composition, including mobile augmented reality (AR) applications. Prior work often targets recovering lighting from the physical environment to support photorealistic AR rendering. Because the common workflow is to use a back-facing camera to capture the physical world for overlaying virtual objects, we refer to this usage pattern as back-facing AR. However, existing methods often fall short in supporting emerging front-facing mobile AR applications, e.g., virtual try-on where a user leverages a front-facing camera to explore the effect of various products (e.g., glasses or hats) of different styles. This lack of support can be attributed to the unique challenges of obtaining 360$^\circ$ HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques. In this paper, we propose to leverage dual-camera streaming to generate a high-quality environment map by combining multi-view lighting reconstruction and parametric directional lighting estimation. Our preliminary results show improved rendering quality using a dual-camera setup for front-facing AR compared to a commercial solution.