Accurate refraction, thanks to raytracing, has always been a popular effect in computer graphics. However, devising a technique that produces realistic refractions at interactive rates remains an open problem.In this paper, a method to achieve realistic and interactive refractive effects through complex static geometry is proposed. It relies on an offline step where many light paths through the object are preevaluated. During rendering, these precomputed paths are used to provide approximations of actual refracted paths through the geometry, enabling further sampling of an environment map. The relevant information of the light paths, namely final output direction when leaving refractive object, is compressed using frequency domain based spherical harmonics. The matching decompression procedure, entirely offloaded onto graphics hardware, is handled at interactive speed.

parametric colour functions are widely used in Image-Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least-squares-based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image-based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade-off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling.

Abstract. Modeling the flowing blood in vascular structures is crucial to perform in silico simulations in various clinical contexts. This remains however an emerging and challenging research field, that raises several open issues. In particular, a compromise is generally made between the completeness of the simulation and the complicated architecture of the vasculature: reduced order simulations (lumped parameter models) represent vascular networks, whereas detailled models are devoted to small regions of interest. However, technical improvements enable targeting of compartments of the blood circulation rather than focusing on vascular branched segments. This article aims at investigating the cerebral flow in the entire venous drainage that can be reconstructed from medical imaging.

Kitware SAS, France / USA Abstract. Angiographic imaging is a crucial domain of medical imaging. In particular, Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source / open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies.

Figure 1: Left image represents a view with a perspective projection. Right image shows and example of the hybrid projection. In left image the user cannot see the chair behind him.

Figure 1: Left image represents a view with a perspective projection. Right image shows and example of the hybrid projection. In left image the user cannot see the chair behind him.