We demonstrate multi-scale multi-parameter optical coherence tomography (OCT) imaging and visualization of Johannes Vermeer's painting Girl with a Pearl Earring. Through automated acquisition, OCT image segmentation, and 3D volume stitching we realize OCT imaging at the scale of an entire painting. This makes it possible to image, with micrometer axial and lateral resolution, an entire painting over more than 5 orders of length scale. From the multi-scale OCT data we quantify multiple parameters in a fully automated way: the surface height, the scattering strength, and the combined glaze and varnish layer thickness. The multiparameter OCT data of Girl with a Pearl Earring shows various features: Vermeer's brushstrokes, surface craquelure, paint losses, and restorations. Through an interactive visualization of the Girl, based on the OCT data and the optical properties of historical reconstructions of Vermeer's paint, we can virtually study the effect of the lighting condition, viewing angle, zoom level and presence/absence of glaze layer. The interactive visualization shows various new painting features. It demonstrates that the glaze layer structure and its optical properties were essential to Vermeer to create an extremely strong light to dark contrast between the figure and the background that gives the painting such an iconic aesthetic appeal.

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We present a general sample reweighting scheme and its underlying theory for the integration of an unknown function with low dimensionality. Our method produces be er results than standard weighting schemes for common sampling strategies, while avoiding bias. Our main insight is to link the weight derivation to the function reconstruction process during integration. e implementation of our solution is simple and results in an improved convergence behavior. We illustrate its bene t by applying our method to multiple Monte Carlo rendering problems.

Monte-Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni-directional path tracer, and light-subpath sampling in Bi-Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by~20% compared to other state-of-the-art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence.

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