Correlated Photon Mapping for Interactive Global Illumination of Time-Varying Volumetric Data

IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 23, Number 1, page 901--910 - jan 2017
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We present a method for interactive global illumination of both static and time-varying volumetric data based on reduction of the overhead associated with re-computation of photon maps. Our method uses the identification of photon traces invariant to changes of visual parameters such as the transfer function (TF), or data changes between time-steps in a 4D volume. This lets us operate on a variant subset of the entire photon distribution. The amount of computation required in the two stages of the photon mapping process, namely tracing and gathering, can thus be reduced to the subset that are affected by a data or visual parameter change. We rely on two different types of information from the original data to identify the regions that have changed. A low resolution uniform grid containing the minimum and maximum data values of the original data is derived for each time step. Similarly, for two consecutive time-steps, a low resolution grid containing the difference between the overlapping data is used. We show that this compact metadata can be combined with the transfer function to identify the regions that have changed. Each photon traverses the low-resolution grid to identify if it can be directly transferred to the next photon distribution state or if it needs to be recomputed. An efficient representation of the photon distribution is presented leading to an order of magnitude improved performance of the raycasting step. The utility of the method is demonstrated in several examples that show visual fidelity, as well as performance. The examples show that visual quality can be retained when the fraction of retraced photons is as low as 40%-50%.

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BibTex references

@article{JY17,
  author       = {J{\"o}nsson, Daniel and Ynnerman, Anders},
  title        = {{Correlated Photon Mapping for Interactive Global Illumination of Time-Varying Volumetric Data}},
  journal      = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  number       = {1},
  volume       = {23},
  pages        = {901--910},
  year         = {2017}
}

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