CBE Research Portal

May 15, 2019

Computing Long-term Luminance Maps from Limited Number of HDR Imagery

Completed in 2017.

This research focuses on the development of a novel prediction model to generate annual luminance maps of indoor space from a subset of images by using deep neural networks (DNN). The results show that by only rendering 5% of annual luminance maps, the proposed DNNs model can predict the rest with comparable accuracy that closely matches those high-quality point-in-time renderings generated by Radiance software. This model can be applied to accelerate annual luminance-based simulations and lays the groundwork for generating annual luminance maps utilizing High Dynamic Range captures of existing environments.