“A spherical cap preserving parameterization for spherical distributions” by Dupuy, Heitz and Belcour

  • ©Jonathan Dupuy, Eric Heitz, and Laurent Belcour




    A spherical cap preserving parameterization for spherical distributions

Session/Category Title: Random Sampling




    We introduce a novel parameterization for spherical distributions that is based on a point located inside the sphere, which we call a pivot. The pivot serves as the center of a straight-line projection that maps solid angles onto the opposite side of the sphere. By transforming spherical distributions in this way, we derive novel parametric spherical distributions that can be evaluated and importance-sampled from the original distributions using simple, closed-form expressions. Moreover, we prove that if the original distribution can be sampled and/or integrated over a spherical cap, then so can the transformed distribution. We exploit the properties of our parameterization to derive efficient spherical lighting techniques for both real-time and offline rendering. Our techniques are robust, fast, easy to implement, and achieve quality that is superior to previous work.


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