The glossy Copper Hairball was deliberately placed inside of a glass cube to create pathologically difficult light transport under directional illumination by the sun. Unbiased flavors of path tracing - including bidrectional path tracing - can not sample the product of the BSDF and incident illumination well. While radiance-based path guiding offers a significant improvement over unidirectional path tracing and primary-sample-space path-sampling techniques, product-based path guiding is required to fully capture the difficult light transport on the copper hairball itself. We demonstrate that neural networks can leverage additional features, such as the surface normal, to capture the product even on high-frequency geometry such as the hairball (NPG-Product), outperforming competing methods at an equal sample count.

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