Item

Abstract

Pixel processing is becoming increasingly expensive for real-time applications due to the complexity of today's shaders and high-resolution framebuffers. However, most shading results are spatially or temporally coherent, which allows for sparse sampling and reuse of neighboring pixel values. This paper proposes a simple framework for spatio-temporal upsampling on modern GPUs. In contrast to previous work, which focuses either on temporal or spatial processing on the GPU, we exploit coherence in both. Our algorithm combines adaptive motion-compensated filtering over time and geometry-aware upsampling in image space. It is robust with respect to high-frequency temporal changes, and achieves substantial performance improvements by limiting the number of recomputed samples per frame. At the same time, we increase the quality of spatial upsampling by recovering missing information from previous frames. This temporal strategy also allows us to ensure that the image converges to a higher quality result.