Bieron, JamesPeers, PieterKlein, Reinhard and Rushmeier, Holly2020-08-232020-08-232020978-3-03868-108-32309-5059https://doi.org/10.2312/mam.20201137https://diglib.eg.org:443/handle/10.2312/mam20201137Image-based BRDF matching is a special case of inverse rendering, where the parameters of a BRDF model are optimized based on a photograph of a homogeneous material under natural lighting. Using a perceptual image metric, directly optimizing the difference between a rendering and a reference image can provide a close visual match between the model and reference material. However, perceptual image metrics rely on image-features and thus require full resolution renderings that can be costly to produce especially when embedded in a non-linear search procedure for the optimal BRDF parameters. Using a pixel-based metric, such as the squared difference, can approximate the image error from a small subset of pixels. Unfortunately, pixel-based metrics are often a poor approximation of human perception of the material's appearance. We show that comparable quality results to a perceptual metric can be obtained using an adaptive pixel-based metric that is optimized based on the appearance similarity of the material. As the core of our adaptive metric is pixel-based, our method is amendable to imagesubsampling, thereby greatly reducing the computational cost.10.2312/mam.202011371-4