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HDR reconstruction is an important task in computer vision with manyindustrial needs. The traditional approaches merge multiple exposure shots togenerate HDRs that correspond to the physical quantity of illuminance of thescene. However, the tedious capturing process makes such multi-shot approachesinconvenient in practice. In contrast, recent single-shot methods predict avisually appealing HDR from a single LDR image through deep learning. But it isnot clear whether the previously mentioned physical properties would stillhold, without training the network to explicitly model them. In this paper, weintroduce the physical illuminance constraints to our single-shot HDRreconstruction framework, with a focus on spherical panoramas. By the proposedphysical regularization, our method can generate HDRs which are not onlyvisually appealing but also physically plausible. For evaluation, we collect alarge dataset of LDR and HDR images with ground truth illuminance measures.Extensive experiments show that our HDR images not only maintain high visualquality but also top all baseline methods in illuminance prediction accuracy.

Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas