Efficient Progressive Radiance Estimation Engine Architecture and Implementation for Progressive Photon Mapping

This paper introduces a multiple photon sampling technique based on stochastic progressive photon mapping. We use the image space concept to divide the scene into continuous sub-blocks and then we calculate our proposed distance function and photon number function in each of the sub-blocks. The distance function is used to calculate the distance error of the hit point and to determine whether each sub-block is located at a boundary between different objects. The photon number function is used to calculate the photon number error and to determine whether the photon distribution in each sub-block is uniform. Based on the values of the distance error and the photon number error, the multiple photon sampling technique is used to acquire multiple samples of the hit point in each sub-block. Instead of using a single radius for the radiance estimate, we use three different radii and compute the final radiance estimate as a weighted average of the three values. When compared with the existing stochastic progressive photon mapping method, our method provides a better solution to the photon distribution problem and can also reduce bias and noise, especially in the scene with drastic changes in light and dark.

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Multiple Photon Sampling Technique Based on Stochastic Progressive Photon Mapping

Yang

Kang

2020

J. Phys.: Conf. Ser.

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Efficient Progressive Radiance Estimation Engine Architecture and Implementation for Progressive Photon Mapping

Cited by 1 publication

References 16 publications

Multiple Photon Sampling Technique Based on Stochastic Progressive Photon Mapping

Multiple Photon Sampling Technique Based on Stochastic Progressive Photon Mapping

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