Computing Long-term Daylighting Simulations from High Dynamic Range Imagery Using Deep Neural Networks.
Yue Liu, Alex Colburn, Mehlika Inanici, IBPSA 2019 Conference, Rome, Italy, September 2-4, 2019.
Paper: http://www.ibpsa.org/proceedings/BS2019/BS2019_210369.pdf
Code and Data coming soon!
Annual luminance maps provide meaningful evaluations for occupants’ visual comfort, preferences, and perception. However, acquiring luminance maps require labor-intensive and time-consuming simulations or impracticable long-term field measurements. This paper presents a novel method to accelerate annual luminance-based evaluations utilizing a deep neural network (DNN). From a small subset (5%) of high dynamic range (HDR) imagery, our method can predict annual panoramic luminance maps (with 360-degrees horizontal and 180-degrees vertical field of view) within an hour. Unlike the fixed camera viewpoint of perspective or fisheye projections that are commonly used in daylighting evaluations, panoramas allow full degree-of-freedom in camera roll, pitch, and yaw, thus providing a robust source of information for an occupant’s visual experience in a given environment. The DNN predicted high-quality panoramas are validated against Radiance RPICT renderings using a series of quantitative and qualitative metrics. With the developed workflow, practitioners and researchers can incorporate long-term luminance-based metrics over multiple view directions into the design and research process without the lengthy computing processes.