| Antialiasing + Depth of Field |
Antialiasing + Depth of Field + Motion Blur |
Antialiasing + Depth of Field + Environment Lighting |
Antialiasing + Depth of Field + Area Lighting + Diffuse Interreflections |
 |
 |
 |
 |
 |
 |
 |
 |
| Average 32 samples per pixel |
Average 32 samples per pixel | Average 32 samples per pixel | Average 32 samples per pixel |
| 5.05 minutes |
16.23 minutes |
34.37 minutes |
15 minutes |
| Abstract |
| Effects such as depth of field,
area lighting, antialiasing and global illumination require evaluating
a complex high-dimensional integral at each pixel of an image. We
develop a new adaptive rendering algorithm that greatly reduces the
number of samples needed for Monte Carlo integration. Our method
renders directly into an image-space wavelet basis. First, we
adaptively distribute Monte Carlo samples to reduce the variance of the
wavelet basis' scale coefficients, while using the wavelet coefficients
to find edges. Working in wavelets, rather than pixels, allows us
to sample not only image-space edges but also other features that are
smooth in the image plane but have high variance in other integral
dimensions. In the second stage, we reconstruct the image from
these samples by using a suitable wavelet approximation. We
achieve this by subtracting an estimate of the error in each wavelet
coefficient from its magnitude, effectively producing the smoothest
image consistent with the rendering samples. Our algorithm
renders scenes with significantly fewer samples than basic Monte Carlo
or adaptive techniques. Moreover, the method introduces minimal
overhead, and can be efficiently included in an optimized ray-tracing
system. |
|
