This paper introduces a new error metric for irradiance caching that significantly outperforms the classic Split-Sphere heuristic. Our new error metric builds on recent work using second order gradients (Hessians) as a principled error bound for the irradiance. We add occlusion information to the Hessian computation, which greatly improves the accuracy of the Hessian in complex scenes, and this makes it possible for the first time to use a radiometric error metric for irradiance caching. We enhance the metric making it based on the relative error in the irradiance as well as robust in the presence of black occluders. The resulting error metric is efficient to compute, numerically robust, supports elliptical error bounds and arbitrary hemispherical sample distributions, and unlike the Split-Sphere heuristic it is not necessary to arbitrarily clamp the computed error thresholds. Our results demonstrate that the new error metric outperforms existing error metrics based on the Split-Sphere model and occlusion-unaware Hessians.
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