Chalmers, AndrewZickler, ToddRhee, TaehyunLee, Sung-hee and Zollmann, Stefanie and Okabe, Makoto and Wuensche, Burkhard2020-10-292020-10-292020978-3-03868-120-5https://doi.org/10.2312/pg.20201223https://diglib.eg.org:443/handle/10.2312/pg20201223Radiance maps (RM) are used for capturing the lighting properties of real-world environments. Databases of RMs are useful for various rendering applications such as Look Development, live action composition, mixed reality, and machine learning. Such databases are not useful if they cannot be organized in a meaningful way. To address this, we introduce the illumination space, a feature space that arranges RM databases based on illumination properties. We avoid manual labeling by automatically extracting features from an RM that provides a concise and semantically meaningful representation of its typical lighting effects. This is made possible with the following contributions: a method to automatically extract a small set of dominant and ambient lighting properties from RMs, and a low-dimensional (5D) light feature vector summarizing these properties to form the illumination space. Our method is motivated by how the RM illuminates the scene as opposed to describing the textural content of the RM.10.2312/pg.202012237-12