Abstract
Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path-traced results, but with a greatly reduced computational complexity, allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.
Highlights
This article is an extension to our publication at the 37th Computer Graphics & Visual ComputingGathering 2019 [1].Global illumination (GI) rendering based on Monte Carlo (MC) methods allows for the generation images of astonishing realism that can often hardly be distinguished from real photographs
We show that our approach performs comparably to much higher sampling rates in path tracing regarding relative mean square error and multi-scale structural similarity (MS-SSIM) metrics
To determine the visual error, we rendered the scenes Country Kitchen (CK) and Streets of Asia (SoA) using a camera fly-through of 500 frames with different configurations of the HashCache and regular path tracing for comparison
Summary
Global illumination (GI) rendering based on Monte Carlo (MC) methods allows for the generation images of astonishing realism that can often hardly be distinguished from real photographs. Even though these methods have been around for a long time, their computational complexity remains a major challenge. Numerous methods have been introduced that help to increase the visual quality by filtering the noise from images rendered with low sample counts. These methods often result in visually pleasing, noise-free images. When moving through the Computers 2020, 9, 17; doi:10.3390/computers9010017 www.mdpi.com/journal/computers
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