Synthesizing realistic images involves computing high-dimensional light transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics, and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods.
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Thanks to all the anonymous reviewers for their comments and thoughtful guidance. Our results show scenes (summarized in Figure 12) coming from third parties. We acknowledge the PBRT scene repository for San Miguel and Bathroom. Wooden staircase, Modern hall, Modern living room, Japanese classroom, White room, Grey & white room, and Utah teapot have been provided by Benedikt Bitterli.
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