Double Hierarchies for Eicient Sampling in Monte Carlo Rendering

نویسندگان

  • Norbert Bus
  • Tamy Boubekeur
چکیده

We propose a novel representation of the light €eld tailored to improve importance sampling for Monte Carlo rendering. Œe domain of the light €eld i.e., the product space of spatial positions and directions is hierarchically subdivided into subsets on which local models characterize the light transport. Œe data structure is based on double trees, and only approximates the exact light €eld, but enables ecient queries for importance sampling and easy setup by tracing photons in the scene. Œe framework is simple yet ƒexible, supports any type of local model for representing the light €eld, provided it can be eciently importance sampled, and progressive re€nement with an arbitrary number of photons. Last, we provide a reference open source implementation of our method.

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تاریخ انتشار 2017