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GPU-Based Real-Time Light Source Estimation for Augmented Reality
Soyoung Park, Sunghun Jo, Sungkil Lee
http://doi.org/10.5626/JOK.2019.46.1.1
To render synthetic objects on static images captured from the real world, estimation of illumination from the images and application for rendering the objects is solicited. In this work, we propose a real-time estimation of light sources, when the 3D-reconstructed geometry of an indoor scene and the corresponding textures are available. To restore the high intensity of the light source, we first convert an LDR image into HDR. The converted image is then used to get 2D positions of the light sources by hierarchical division-based sampling technique. Lastly, 3D positions of the light sources are estimated from texture-geometry mapping. Since the sampling technique stores grid areas generated at each stage in mipmap textures, the division of areas is processed on GPU in a parallel way, which makes it work in real-time. Our approach can be used in rendering where explicit positions of the light sources are asked, such as shadows.
Real-Time Nonlinear Lens-Flare Rendering Method Based on Look-Up Table
Sunghun Jo, Yuna Jeong, Sungkil Lee
In computer graphics, high-quality lens flares have been generated using costly offline rendering. A recent matrix-based approximation has enabled generation of high-quality lens flares suitable for real-time applications, but its quality degrades due to the lack of nonlinear patterns of lens flares. This paper introduces a method for high-quality lens-flare rendering, which includes blending of both nonlinear as well as linear patterns. The nonlinear patterns are pre-rendered or photographically captured offline and stored in a look-up table. The online stage reads only the pattern by looking up the table using a light angle, hence making its performance drop negligible while greatly improving the quality.
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