Neural Product Importance Sampling via Warp Composition
<|reference_start|>Neural Product Importance Sampling via Warp Composition: Achieving high efficiency in modern photorealistic rendering hinges on using Monte Carlo sampling distributions that closely approximate the illumination integral estimated for every pixel. Samples are typically generated from a set of simple distributions, each targeting a different factor in the integrand, which are combined via multiple importance sampling. The resulting mixture distribution can be far from the actual product of all factors, leading to sub-optimal variance even for direct-illumination estimation. We present a learning-based method that uses normalizing flows to efficiently importance sample illumination product integrals, e.g., the product of environment lighting and material terms. Our sampler composes a flow head warp with an emitter tail warp. The small conditional head warp is represented by a neural spline flow, while the large unconditional tail is discretized per environment map and its evaluation is instant. If the conditioning is low-dimensional, the head warp can be also discretized to achieve even better performance. We demonstrate variance reduction over prior methods on a range of applications comprising complex geometry, materials and illumination.<|reference_end|>
@article{litalien2024neural,
    title={Neural Product Importance Sampling via Warp Composition},
    author={Joey Litalien, Milov{s} Hav{s}an, Fujun Luan, Krishna Mullia, and
  Iliyan Georgiev},
    journal={arXiv preprint arXiv:2409.18974},
    year={2024},
    doi={10.1145/3680528.3687566},
    archivePrefix={arXiv},
    eprint={2409.18974},
    primaryClass={cs.CV cs.GR}
}