Path tracing produces realistic results including global illumination using a unified simple rendering pipeline. Reducing the amount of noise to imperceptible levels without post-processing requires thousands of
samples per pixel
(spp), while currently it is only possible to render extremely noisy 1 spp frames in real time with desktop GPUs. However, post-processing can utilize feature buffers, which contain noise-free auxiliary data available in the rendering pipeline. Previously, regression-based noise filtering methods have only been used in offline rendering due to their high computational cost. In this article we propose a novel regression-based reconstruction pipeline, called
Blockwise Multi-Order Feature Regression
(BMFR), tailored for path-traced 1 spp inputs that runs in real time. The high speed is achieved with a fast implementation of augmented QR factorization and by using stochastic regularization to address rank-deficient feature data. The proposed algorithm is 1.8× faster than the previous state-of-the-art real-time path-tracing reconstruction method while producing better quality frame sequences.
Multithreading is an important software modularization technique. However, it can incur substantial overheads, especially in processors where the amount of architecturally visible state is large.We propose an implementation technique for cooperative multithreading, where context switches occur in places that minimize the amount of state that needs to be saved. The subset of processor state saved during each context switch is based on where the switch occurs.We have validated the approach by an empirical study of resource usage in basic blocks, and by implementing the co-operative threading in our compiler. Performance figures are given for an MP3 player utilizing the threading implementation.
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