Browsing by Author "Bieron, James"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    An Adaptive Metric for BRDF Appearance Matching
    (The Eurographics Association, 2020) Bieron, James; Peers, Pieter; Klein, Reinhard and Rushmeier, Holly
    Image-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.
  • Thumbnail Image
    Item
    Deep Separation of Direct and Global Components from a Single Photograph under Structured Lighting
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Duan, Zhaoliang; Bieron, James; Peers, Pieter; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    We present a deep learning based solution for separating the direct and global light transport components from a single photograph captured under high frequency structured lighting with a co-axial projector-camera setup. We employ an architecture with one encoder and two decoders that shares information between the encoder and the decoders, as well as between both decoders to ensure a consistent decomposition between both light transport components. Furthermore, our deep learning separation approach does not require binary structured illumination, allowing us to utilize the full resolution capabilities of the projector. Consequently, our deep separation network is able to achieve high fidelity decompositions for lighting frequency sensitive features such as subsurface scattering and specular reflections. We evaluate and demonstrate our direct and global separation method on a wide variety of synthetic and captured scenes.
  • Thumbnail Image
    Item
    Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lighting
    (The Eurographics Association, 2019) Cooper, Victoria L.; Bieron, James C.; Peers, Pieter; Klein, Reinhard and Rushmeier, Holly
    In this paper we demonstrate robust estimation of the model parameters of a fully-linear data-driven BRDF model from a reflectance map under known natural lighting. To regularize the estimation of the model parameters, we leverage the reflectance similarities within a material class. We approximate the space of homogeneous BRDFs using a Gaussian mixture model, and assign a material class to each Gaussian in the mixture model. Next, we compute a linear solution per material class. Finally, we select the best candidate as the final estimate. We demonstrate the efficacy and robustness of our method using the MERL BRDF database under a variety of natural lighting conditions.