Zhou, ZhiqianZhang, ChengDong, ZhaoMarshall, CarlZhao, ShuangHaines, EricGarces, Elena2024-06-252024-06-252024978-3-03868-262-21727-3463https://doi.org/10.2312/sr.20241149https://diglib.eg.org/handle/10.2312/sr20241149The inference of material reflectance from physical observations (e.g., photographs) is usually under-constrained, causing point estimates to suffer from ambiguity and, thus, generalize poorly to novel configurations. Conventional methods address this problem by using dense observations or introducing priors. In this paper, we tackle this problem from a different angle by introducing a method to quantify uncertainties. Based on a Bayesian formulation, our method can quantitatively analyze how under-constrained a material inference problem is (given the observations and priors), by sampling the entire posterior distribution of material parameters rather than optimizing a single point estimate as given by most inverse rendering methods. Further, we present a method to guide acquisition processes by recommending viewing/lighting configurations for making additional observations. We demonstrate the usefulness of our technique using several synthetic and one real example.Attribution 4.0 International License!-!10.2312/sr.2024114911 pages