Figure 1: High quality all-hex meshes of complex shapes automatically generated by our method and the PolyCubes we compute to create them. For the kiss both fine and coarse meshes are shown. AbstractWhile hexahedral mesh elements are preferred by a variety of simulation techniques, constructing quality all-hex meshes of general shapes remains a challenge. An attractive hex-meshing approach, often referred to as submapping, uses a low distortion mapping between the input model and a PolyCube (a solid formed from a union of cubes), to transfer a regular hex grid from the PolyCube to the input model. Unfortunately, the construction of suitable PolyCubes and corresponding volumetric maps for arbitrary shapes remains an open problem. Our work introduces a new method for computing low-distortion volumetric PolyCube deformations of general shapes and for subsequent all-hex remeshing. For a given input model, our method simultaneously generates an appropriate PolyCube structure and mapping between the input model and the PolyCube. From these we automatically generate good quality all-hex meshes of complex natural and man-made shapes.

Transient imaging is an exciting a new imaging modality that can be used to understand light propagation in complex environments, and to capture and analyze scene properties such as the shape of hidden objects or the reflectance properties of surfaces. Unfortunately, research in transient imaging has so far been hindered by the high cost of the required instrumentation, as well as the fragility and difficulty to operate and calibrate devices such as femtosecond lasers and streak cameras. In this paper, we explore the use of photonic mixer devices (PMD), commonly used in inexpensive time-of-flight cameras, as alternative instrumentation for transient imaging. We obtain a sequence of differently modulated images with a PMD sensor, impose a model for local light/object interaction, and use an optimization procedure to infer transient images given the measurements and model. The resulting method produces transient images at a cost several orders of magnitude below existing methods, while simultaneously simplifying and speeding up the capture process.

We present a novel approach for highly detailed 3D imaging of turbulent fluid mixing behaviors. The method is based on visible light computed tomography, and is made possible by a new stochastic tomographic reconstruction algorithm based on random walks. We show that this new stochastic algorithm is competitive with specialized tomography solvers such as SART, but can also easily include arbitrary convex regularizers that make it possible to obtain high-quality reconstructions with a very small number of views. Finally, we demonstrate that the same stochastic tomography approach can also be used to directly re-render arbitrary 2D projections without the need to ever store a 3D volume grid.

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Cinema projectors need to compete with home theater displays in terms of image quality. High frame rate and spatial resolution as well as stereoscopic 3D are common features today, but even the most advanced cinema projectors lack in-scene contrast and, more important, high peak luminance, both of which are essential perceptual attributes of images appearing realistic. At the same time, HDR image statistics suggest that the average image intensity in a controlled ambient viewing environment such as the cinema can be as low as 1% for cinematic HDR content and not often higher than 18%, middle gray in photography. Traditional projection systems form images and colors by blocking the source light from a lamp, therefore attenuating between 99% and 82% of light, on average. This inefficient use of light poses significant challenges for achieving higher peak brightness levels.In this work, we propose a new projector architecture built around commercially available components, in which light can be steered to form images. The gain in system efficiency significantly reduces the total cost of ownership of a projector (fewer components and lower operating cost), and at the same time increases peak luminance and improves black level beyond what is practically achievable with incumbent projector technologies. At the heart of this computational display technology is a new projector hardware design using phase modulation in combination with a new optimization algorithm that is capable of on-the-fly computation of freeform lens surfaces.

We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including nonconvex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.

Velocity reconstruction & advection Figure 1: Low-resolution captures obtained by tomographic scanning (left) are used as inputs to our method which estimates physically plausible dense velocity fields. Such velocity fields fully determine the fluid state and can be applied in a variety of applications including fluid super-resolution (right) allowing capture to be integrated into pipelines for visual effects simulation.

Figure 1: Our modular Primal-Dual optimization method can be applied to fluid guiding (left, right) and to simulate liquids with separating boundary conditions (center). AbstractWe apply a novel optimization scheme from the image processing and machine learning areas, a fast Primal-Dual method, to achieve controllable and realistic fluid simulations. While our method is generally applicable to many problems in fluid simulations, we focus on the two topics of fluid guiding and separating solid-wall boundary conditions. Each problem is posed as an optimization problem and solved using our method, which contains acceleration schemes tailored to each problem. In fluid guiding, we are interested in partially guiding fluid motion to exert control while preserving fluid characteristics. With our method, we achieve explicit control over both large-scale motions and small-scale details which is valuable for many applications, such as level-of-detail adjustment (after running the coarse simulation), spatially varying guiding strength, domain modification, and resimulation with different fluid parameters. For the separating solid-wall boundary conditions problem, our method effectively eliminates unrealistic artifacts of fluid crawling up solid walls and sticking to ceilings, requiring few changes to existing implementations. We demonstrate the fast convergence of our Primal-Dual method with a variety of test cases for both model problems.