SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Physically Based Rendering: From Theory to Implementation
Physically Based Rendering: From Theory to Implementation
Statistical acceleration for animated global illumination
ACM SIGGRAPH 2006 Papers
Multidimensional adaptive sampling and reconstruction for ray tracing
ACM SIGGRAPH 2008 papers
Compressive light transport sensing
ACM Transactions on Graphics (TOG)
Compressive Structured Light for Recovering Inhomogeneous Participating Media
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Frequency analysis and sheared reconstruction for rendering motion blur
ACM SIGGRAPH 2009 papers
ACM SIGGRAPH Asia 2009 papers
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Compressive Rendering: A Rendering Application of Compressed Sensing
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
On filtering the noise from the random parameters in Monte Carlo rendering
ACM Transactions on Graphics (TOG)
Compressive rendering of multidimensional scenes
Proceedings of the 2010 international conference on Video Processing and Computational Video
A theory of monte carlo visibility sampling
ACM Transactions on Graphics (TOG)
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In rendering applications, we are often faced with the problem of computing the integral of an unknown function. Typical approaches used to estimate these integrals are often based on Monte Carlo methods that slowly converge to the correct answer after many point samples have been taken. In this work, we study this problem under the framework of compressed sensing and reach the conclusion that if the signal is sparse in a transform domain, we can evaluate the integral accurately using a small set of point samples without requiring the lengthy iterations of Monte Carlo approaches. We demonstrate the usefulness of our framework by proposing novel algorithms to address two problems in computer graphics: image antialiasing and motion blur. We show that we can use our framework to generate good results with fewer samples than is possible with traditional approaches.