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
Compressive estimation for signal integration in rendering
EGSR'10 Proceedings of the 21st Eurographics conference on Rendering
Adaptive image synthesis for compressive displays
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
5D Covariance tracing for efficient defocus and motion blur
ACM Transactions on Graphics (TOG)
Rendering in shift-invariant spaces
Proceedings of Graphics Interface 2013
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Recently, there has been growing interest in compressed sensing (CS), the new theory that shows how a small set of linear measurements can be used to reconstruct a signal if it is sparse in a transform domain. Although CS has been applied to many problems in other fields, in computer graphics, it has only been used so far to accelerate the acquisition of light transport. In this paper, we propose a novel application of compressed sensing by using it to accelerate ray-traced rendering in a manner that exploits the sparsity of the final image in the wavelet basis. To do this, we raytrace only a subset of the pixel samples in the spatial domain and use a simple, greedy CS-based algorithm to estimate the wavelet transform of the image during rendering. Since the energy of the image is concentrated more compactly in the wavelet domain, less samples are required for a result of given quality than with conventional spatial-domain rendering. By taking the inverse wavelet transform of the result, we compute an accurate reconstruction of the desired final image. Our results show that our framework can achieve high-quality images with approximately 75 percent of the pixel samples using a nonadaptive sampling scheme. In addition, we also perform better than other algorithms that might be used to fill in the missing pixel data, such as interpolation or inpainting. Furthermore, since the algorithm works in image space, it is completely independent of scene complexity.