Computer Graphics

University of California - Berkeley

Axis-Aligned Filtering for Interactive Sampled Soft Shadows


We develop a simple and efficient method for soft shadows from planar area light sources, based on explicit occlusion calculation by raytracing, followed by adaptive image-space filtering. Since the method is based on Monte Carlo sampling, it is accurate. Since the filtering is in image-space, it adds minimal overhead and can be performed at real-time frame rates. We obtain interactive speeds, using the Optix GPU raytracing framework. Our technical approach derives from recent work on frequency analysis and sheared pixel-light filtering for offline soft shadows. While sample counts can be reduced dramatically, the sheared filtering step is slow, adding minutes of overhead. We develop the theoretical analysis to instead consider axis-aligned filtering, deriving the sampling rates and filter sizes. We also show how the filter size can be reduced as the number of samples increases, ensuring a consistent result that converges to ground truth as in standard Monte Carlo rendering.


Soham Mehta, Brandon Wang, and Ravi Ramamoorthi. "Axis-Aligned Filtering for Interactive Sampled Soft Shadows". ACM Trans. Graph., 31(6):163:1–163:10, Nov 2012.

Supplemental Material


Siggraph Asia paper PDF(7 MB)

Source Code

Source Code (1 MB)

Figure 1

Soft shadows for the 'tentacles' scene: (a) and (c) Our method, 23 spp (b) equal samples, no filtering, 23 spp (d) ground truth (e) equal RMS error for unfiltered 200 spp still has some noise

Video (88 MB)