Papers by Pradeep Sen


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Scalable Laplacian Eigenfluids

Qiaodong Cui, Pradeep Sen, Theodore Kim
August 2018

The Laplacian Eigenfunction method for fluid simulation, which we refer to as Eigenfluids, introduced an elegant new way to capture intricate fluid flows with near-zero viscosity. However, the approach does not scale well, as the memory cost grows prohibitively with the number of eigenfunctions. The method also lacks generality, because ... more

Paper (PDF)

Additional materials: [ScalableEigenFluidSupplementalVideo.mp4], [supplement.pdf], [ScalableEigenFluidMainVideo.mp4]


Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings

Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novak, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle
July 2017

Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit to noise in the input. For this reason, supervised learning methods have been proposed that ... more

Paper (PDF)

SIGGRAPH 2017


Compressing Fluid Subspaces

Aaron Demby-Jones, Pradeep Sen, Theodore Kim
May 2016

Subspace fluid simulations, also known as reduced-order simulations, can be extremely fast, but also require basis matrices that consume an enormous amount of memory. Motivated by the extreme sparsity of Laplacian eigenfunctions in the frequency domain, we design a frequency-space codec that is capable of compressing basis matrices by up ... more

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