Browsing by Author "Thiery, Jean-Marc"

Now showing 1 - 3 of 3
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    MatMorpher: A Morphing Operator for SVBRDFs
    (The Eurographics Association, 2021) Gauthier, Alban; Thiery, Jean-Marc; Boubekeur, Tamy; Bousseau, Adrien and McGuire, Morgan
    We present a novel morphing operator for spatially-varying bidirectional reflectance distribution functions. Our operator takes as input digital materials modeled using a set of 2D texture maps which control the typical parameters of a standard BRDF model. It also takes an interpolation map, defined over the same texture domain, which modulates the interpolation at each texel of the material. Our algorithm is based on a transport mechanism which continuously transforms the individual source maps into their destination counterparts in a feature-sensitive manner. The underlying non-rigid deformation is computed using an energy minimization over a transport grid and accounts for the user-selected dominant features present in the input materials. During this process, we carefully preserve details by mixing the material channels using a histogram-aware color blending combined with a normal reorientation. As a result, our method allows to explore large regions of the space of possible materials using exemplars as anchors and our interpolation scheme as a navigation mean. We also give details about our real time implementation, designed to map faithfully to the standard physically-based rendering workflow and letting users rule interactively the morphing process.
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    Multi-modal 3D Image Registration Using Interactive Voxel Grid Deformation and Rendering
    (The Eurographics Association, 2022) Richard, Thomas; Chastagnier, Yan; Szabo, Vivien; Chalard, Kevin; Summa, Brian; Thiery, Jean-Marc; Boubekeur, Tamy; Faraj, Noura; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    We introduce a novel multi-modal 3D image registration framework based on 3D user-guided deformation of both volume's shape and intensity values. Being able to apply deformations in 3D gives access to a wide new range of interactions allowing for the registration of images from any acquisition method and of any organ, complete or partial. Our framework uses a state of the art 3D volume rendering method for real-time feedback on the registration accuracy as well as the image deformation. We propose a novel methodological variation to accurately display 3D segmented voxel grids, which is a requirement in a registration context for visualizing a segmented atlas. Our pipeline is implemented in an open-source software (available via GitHub) and was directly used by biologists for registration of mouse brain model autofluorescence acquisition on the Allen Brain Atlas. The latter mapping allows them to retrieve regions of interest properly identified on the segmented atlas in acquired brain datasets and therefore extract only high-resolution images of those areas, avoiding the creation of images too large to be processed.
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    Progressive and Efficient Multi-Resolution Representations for Brain Tractograms
    (The Eurographics Association, 2018) Mercier, Corentin; Gori, Pietro; Rohmer, Damien; Cani, Marie-Paule; Boubekeur, Tamy; Thiery, Jean-Marc; Bloch, Isabelle; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Current tractography methods generate tractograms composed of millions of 3D polylines, called fibers, making visualization and interpretation extremely challenging, thus complexifying the use of this technique in a clinical environment. We propose to progressively simplify tractograms by grouping similar fibers into generalized cylinders. This produces a fine-grained multiresolution model that provides a progressive and real-time navigation through different levels of detail. This model preserves the overall structure of the tractogram and can be adapted to different measures of similarity. We also provide an efficient implementation of the method based on a Delaunay tetrahedralization. We illustrate our method using the Human Connectome Project dataset.