(a) (b) (c) Figure 1: Examples of fluids simulated with our technique: (a) a city block hit by a tsunami (vortex domain in yellow) (b) seagulls flying through smoke (c) smoke flow around a sphere. We achieve up to three orders of magnitude of performance over standard grid-only techniques. AbstractSimulating fluids in large-scale scenes with appreciable quality using state-of-the-art methods can lead to high memory and compute requirements. Since memory requirements are proportional to the product of domain dimensions, simulation performance is limited by memory access, as solvers for elliptic problems are not computebound on modern systems. This is a significant concern for largescale scenes. To reduce the memory footprint and memory/compute ratio, vortex singularity bases can be used. Though they form a compact bases for incompressible vector fields, robust and efficient modeling of nonrigid obstacles and free-surfaces can be challenging with these methods.We propose a hybrid domain decomposition approach that couples Eulerian velocity-based simulations with vortex singularity simulations. Our formulation reduces memory footprint by using smaller Eulerian domains with compact vortex bases, thereby improving the memory/compute ratio, and simulation performance by more than 1000x for single phase flows as well as significant improvements for free-surface scenes. Coupling these two heterogeneous methods also affords flexibility in using the most appropriate method for modeling different scene features, as well as allowing robust interaction of vortex methods with free-surfaces and nonrigid obstacles.

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

We present a real-time algorithm for compressing textures based on low frequency signal modulated (LFSM) texture compression. Our formulation is based on intensity dilation and exploits the notion that the most important features of an image are those with high contrast ratios. We present a simple two pass algorithm for propagating the extremal intensity values that contribute to these contrast ratios into the compressed encoding. We use our algorithm to compress PVRTC textures in real-time and compare our performance with prior techniques in terms of speed and quality.

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Recent advances in computer graphics have relied on high‐quality textures in order to generate photorealistic real‐time images. Texture compression standards meet these growing demands for data, but current texture compression schemes use fixed‐rate methods where statically sized blocks of pixels are represented using the same numbers of bits irrespective of their data content. In order to account for the natural variation in detail, we present an alternative format that allows variable bit‐rate texture compression with minimal changes to texturing hardware. Our proposed scheme uses one additional level of indirection to allow the variation of the block size across the same texture. This single change is exploited to both vary the amount of bits allocated to certain parts of the texture and to duplicate redundant texture information across multiple pixels. To minimize hardware changes, the method picks combinations of block sizes and compression methods from existing fixed‐rate standards. With this approach, our method is able to demonstrate energy savings of up to 50%, as well as higher quality compressed textures over current state of the art techniques.