Physically-inspired Deep Light Estimation from a Homogeneous-Material Object for Mixed Reality Lighting
March 31, 2020 | video, 4980views
Jinwoo Park, Hunmin Park, Sung-Eui Yoon, Woontack Woo, "Physically-inspired Deep Light Estimation from a Homogeneous-Material Object for Mixed Reality Lighting", IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol.26, No.5, May 2020 (also presented in IEEE VR 2020)

In mixed reality (MR), augmenting virtual objects consistently with real-world illumination is one of the key factors that provide a realistic and immersive user experience. For this purpose, we propose a novel deep learning-based method to estimate high dynamic range (HDR) illumination from a single RGB image of a reference object. To obtain illumination of a current scene, previous approaches inserted a special camera in that scene, which may interfere with user's immersion, or they analyzed reflected radiances from a passive light probe with a specific type of materials or a known shape. The proposed method does not require any additional gadgets or strong prior cues, and aims to predict illumination from a single image of an observed object with a wide range of homogeneous materials and shapes. To effectively solve this ill-posed inverse rendering problem, three sequential deep neural networks are employed based on a physically-inspired design. These networks perform end-to-end regression to gradually decrease dependency on the material and shape. To cover various conditions, the proposed networks are trained on a large synthetic dataset generated by physically-based rendering. Finally, the reconstructed HDR illumination enables realistic image-based lighting of virtual objects in MR. Experimental results demonstrate the effectiveness of this approach compared against state-of-the-art methods. The paper also suggests some interesting MR applications in indoor and outdoor scenes.
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