GPU-Based Selective Sparse Sampling for Interactive High-Fidelity Rendering

Abstract

Physically-based renderers can produce highly realistic imagery; however such methods suffer from lengthy execution times, which make them impractical for use in interactive applications. Selective rendering exploits limitations in the human visual system to render images that are perceptually similar to high-fidelity renderings, in a fraction of the time. In this paper, we describe a novel GPU-based selective rendering algorithm that uses density of indirect lighting samples on the image plane as a selective variable. A high-speed saliency-guided mechanism is used to sample and evaluate a set of representative pixels locations on the image plane, yielding a sparse representation of indirect lighting in the scene. An image inpainting algorithm is used to reconstruct a dense representation of the indirect lighting component, which is then combined with the direct lighting component to produce the final rendering. Experimental evaluation demonstrates that our selective rendering algorithm achieves a good speedup when compared to standard interleaved sampling, and is significantly faster than a traditional GPU-based high-fidelity renderer.