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