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Monte Carlo Path Tracing with a Radiosity Preprocess

The first example we consider is a Monte Carlo path tracing (MCPT) method with a radiosity preprocess to reduce variance. In this method the estimates L^p are made as described in Section 3, except that in estimating the incident radiance for diffuse-like surfaces in the indirect illumination integral, values from a radiosity preprocess are used. This is essentially the progressive multi-pass method described by Chen et al. [3], except that the ``light ray'' pass for explicit calculation of caustics is replaced by allowing caustic paths to be followed in the integration of indirect illumination. The ``light ray'' pass can be extremely costly when many light source/specular surface combinations need to be examined. By using an energy preserving filter, the noise that has been the drawback of including caustic paths in the Monte Carlo integration will be reduced.

For this method, ``noise'' pixels will be identified as pixels which have not converged to within a user specified interval +/-D. To be sure that most pixels have converged, we need a minimum value of Mt that will be valid for the majority of pixels. To predict Mt, we need to know typical values of Sp (defined in Eq. 5). We also need to estimate the floating point value visibility threshold Ltvis which will translate into a difference of one unit in Np. The threshold depends on the form of T() in Eq. 3.

To predict Sp and Ltvis we take a small pilot sample (e.g. similar to the pilot sample suggested in [2]) by rendering a 100x100 pixel image with 16 trials per pixel. Since the deviation in our estimates decreases with the square root of the number of trials, the estimate of Sp_Mt for Mt samples will be the average sample deviation Sp_16 we calculate for the pilot sample times sqrt(16/Mt). For the purposes of estimating Ltvis, we assume a simple linear tone operator in which the average image value Lave will be mapped to the middle of the display range, NMID. That is:

Knowing Ltvis and an estimate of Sp, the number of trials required to obtain a result accurate to +/- D is approximately:


We construct our method to require a minimum of samples Mt as given by Eq. 10. (Note, because t(1-alpha/2)(Mt-1) is near 1.0 for alpha = 80% and Mt>16 it does not appear in the estimate in Eq. 10.) Since the value of Mt using this technique is a typical, rather than maximum value, we multiply this number by heuristically determined value of 4 to establish Mt,ceiling to insure that the majority of pixels will converge.

The simple linear tone operator is only assumed for the purpose of estimating the number of samples. The image will still be computed in floating point, and any tone operator can be applied to the result.

For the method just described, a pixel is considered a ``noise'' pixel to be treated by the filter if its sample variation is larger than the acceptable tolerance after Mt,ceiling trials. The excess value at each unconverged pixel is defined as the difference between the pixel radiance and the average of its immediate neighbors. If the excess energy exceeds this average by more than the variation allowed in the original calculations (i.e. DLtvis), it is spread into a region around the unconverged pixel.

Energy could be preserved by simply spreading the excess energy over the entire image. However, that would unnecessarily disrupt some pixels, and all definition of features such as caustics would be lost. To determine the size of a smaller region into which the energy should be spread, we use the criterion that we do not want to introduce non-physical high frequency artifacts into the image. The energy is spread so that the additional radiance at each pixel will be no more than Ltvis.

Any shape filter could be used. For a simple box shaped filter, the calculation is straightforward. The difference between the radiance of the unconverged pixel Lu and the average of its converged neighbors Ln is found. The amount that Lu-Ln exceeds the allowable error level DLtvis is the excess value Lexcess which needs to be distributed. To limit the effect on neighboring pixels, the number of pixels to which the excess is distributed is ceiling(Lexcess/Ltvis).

Only converged pixels in the original solution are used to compute the average. The effect of the filter does not depend on the order in which pixels are traversed. In some instances an unconverged pixel will have no converged neighbors. In this case the filter is applied recursively, with pixels that have had excess energy spread out marked as converged in the next iteration.

The method just described doesn't account for the spectral variation of radiance. If the method were applied for each spectral sample individually, wavelengths for which a pixel had a high variance would be smoothed out, while values at other wavelengths would be left untouched. The result would be ``noisy'' areas tending to turn gray. To avoid this color shift, the luminance of each ``noisy'' pixel is compared to the average luminance of its neighbors, and excess luminance is distributed with the same spectral distribution as the original ``noisy'' pixel.

Figure 4 shows the results of applying the simple box filter to an image generated using MCPT with radiosity preprocess. Two small specular boxes, one blue and one red are located in a yellow-brown room. (The specular boxes do not appear shiny because they reflect the featureless walls of the room). An image rendered using Eq. 10 to determine Mt with D = 5 for a 0 to 255 display device is shown on the left. There are noisy regions in the image due to caustic paths in the indirect illumination calculation as diagrammed in Fig. 1(d). A noisy caustic due to the blue box can be seen on the ceiling near the light. More subtle noisy caustics due to the red box can be seen on the floor near the red box and on the ceiling above the red box. There is also a large penumbra region (as diagrammed in Fig. 1(a)) on the left wall due to the blue box.

- Figure 4

The image after applying the filter is shown in the center. A comparison of the unfiltered and filtered images shows that the filter correctly left features outside of the high noise areas undisrupted. Only a small number of pixels in the penumbra region are changed. The large caustic feature due to the blue box is retained and smoothed out, as are the smaller fainter red caustics.

The image in the lower right is the same scene rendered using a minimum sampling rate that is 25 times higher (i.e. Mt set for D = 1.) Not surprisingly, the filtered image appears somewhat noisier than the high sampling rate image which took 25 times longer to compute. This is because the filter is designed to affect the unconverged ``high noise'' pixels only, not to remove the +/-5 units of variation allowed in the original low sampling rate calculation. However, a comparison of the filtered and high sampling rate images does show that the caustic features preserved by the filter are real.

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