We propose a new adaptive rendering algorithm that enhances the
performance of Monte Carlo ray tracing by reducing the noise, i.e.,
variance, while preserving a variety of high-frequency edges in rendered
images through a novel prediction based reconstruction. To
achieve our goal, we iteratively build multiple, but sparse linear
models. Each linear model has its prediction window, where the
linear model predicts the unknown ground truth image that can be
generated with an infinite number of samples. Our method recursively
estimates prediction errors introduced by linear predictions
performed with different prediction windows, and selects an optimal
prediction window minimizing the error for each linear model.
Since each linear model predicts multiple pixels within its optimal
prediction interval, we can construct our linear models only at a
sparse set of pixels in the image screen. Predicting multiple pixels
with a single linear model poses technical challenges, related to deriving
error analysis for regions rather than pixels, and has not been
addressed in the field. We address these technical challenges, and
our method with robust error analysis leads to a drastically reduced
reconstruction time even with higher rendering quality, compared
to state-of-the-art adaptive methods. We have demonstrated that
our method outperforms previous methods numerically and visually
with high performance ray tracing kernels such as OptiX and
Embree.