A Lightweight Network for Outdoor Illumination Estimation on Mobile Devices
Fuyu Ma, Yinwei Zhan, Haidong Gao · 2021
Outdoor illumination estimation is particularly essential for augmented reality applications. Traditionally, handcraft illumination features are used to recover outdoor illumination such as shadows and highlight areas of the scenes. These features are so dependent on human experience and auxiliary equipments that the performances of these approaches are limited. This paper presents a lightweight network based on MobileNet V3 to estimate outdoor illumination from a single outdoor image. A physically-based illumination model is fitted to the sky regions of outdoor panoramas to generate illumination parameters, and different field of view images are extracted from outdoor panoramas. The images with illumination parameters annotated are then used to train our lightweight network. Experiments show that our method can perform well and is suitable for mobile-based applications.