We demonstrate a method for removing noise from images or other data on curved surfaces. Our approach relies on in-surface diffusion: we formulate both the Gaussian diffusion and Perona-Malik edge-preserving diffusion equations in a surface-intrinsic way. Using the Closest Point Method, a recent technique for solving partial differential equations (PDEs) on general surfaces, we obtain a very simple algorithm where we merely alternate a time step of the usual Gaussian diffusion (and similarly Perona-Malik) in a small 3D volume containing the surface with an interpolation step. The method uses a closest point function to represent the underlying surface and can treat very general surfaces. Experimental results include image filtering on smooth surfaces, open surfaces, and general triangulated surfaces.
From huge tidal waves to complex character interactions, FLIP fluid simulations based on volumes and particles have become a key aspect of a modern water pipeline. OpenVDB has recently introduced efficient sparse volumes to production to reduce storage and memory requirements for volumes. We build on this goal with new lossless and lossy particle compression schemes designed to significantly reduce the memory, storage and I/O bandwidth associated with processing large particle sets for simulation and rendering.
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