41-Issue 2
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Browsing 41-Issue 2 by Author "Boubekeur, Tamy"
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Item Fiblets for Real-Time Rendering of Massive Brain Tractograms(The Eurographics Association and John Wiley & Sons Ltd., 2022) Schertzer, JƩrƩmie; Mercier, Corentin; Rousseau, Sylvain; Boubekeur, Tamy; Chaine, Raphaƫlle; Kim, Min H.We present a method to render massive brain tractograms in real time. Tractograms model the white matter architecture of the human brain using millions of 3D polylines (fibers), summing up to billions of segments. They are used by neurosurgeons before surgery as well as by researchers to better understand the brain. A typical raw dataset for a single brain represents dozens of gigabytes of data, preventing their interactive rendering.We address this challenge with a new GPU mesh shader pipeline based on a decomposition of the fiber set into compressed local representations that we call fiblets. Their spatial coherence is used at runtime to efficiently cull hidden geometry at the task shader stage while synthesizing the visible ones as polyline meshlets in a warp-scale parallel fashion at the mesh shader stage. As a result, our pipeline can feed a standard deferred shading engine to visualize the mesostructures of the brain with various classical rendering techniques, as well as simple interaction primitives. We demonstrate that our algorithm provides real-time framerates on very large tractograms that were out of reach for previous methods while offering a fiber-level granularity in both rendering and interaction.Item MaterIA: Single Image High-Resolution Material Capture in the Wild(The Eurographics Association and John Wiley & Sons Ltd., 2022) Martin, Rosalie; Roullier, Arthur; Rouffet, Romain; Kaiser, Adrien; Boubekeur, Tamy; Chaine, Raphaƫlle; Kim, Min H.We propose a hybrid method to reconstruct a physically-based spatially varying BRDF from a single high resolution picture of an outdoor surface captured under natural lighting conditions with any kind of camera device. Relying on both deep learning and explicit processing, our PBR material acquisition handles the removal of shades, projected shadows and specular highlights present when capturing a highly irregular surface and enables to properly retrieve the underlying geometry. To achieve this, we train two cascaded U-Nets on physically-based materials, rendered under various lighting conditions, to infer the spatiallyvarying albedo and normal maps. Our network processes relatively small image tiles (512x512 pixels) and we propose a solution to handle larger image resolutions by solving a Poisson system across these tiles. We complete this pipeline with analytical solutions to reconstruct height, roughness and ambient occlusion.