Dipole Diffusion Error in Thin Geometry: Optical Thickness Laws for Grid-Free Subsurface Scattering
摘要
The dipole and its descendants model subsurface scattering with a radial reflectance profile fitted to a flat, semi-infinite slab. This assumption introduces a systematic geometry error on thin and curved objects. We isolate the effect by comparing the dipole with the finite-slab multipole under the same diffusion model and boundary condition. In slab geometry the diffuse-albedo error has a material-independent leading rate, with , while the prefactor remains material dependent; the same image series gives the transmitted flux, whose leading decay is . We give the closed-form albedo and transmittance, relate the exponents to killed random walks, and extend the interpretation to spatially varying media through optical distance. A brute-force volumetric path tracer fits a reflectance-deficit rate of 1.99 and a transmittance rate of 0.99, matching the round-trip and single-pass predictions. The resulting thickness predictor is a useful thin-feature heuristic, but stress tests show that curvature and illumination can dominate away from the slab setting. For the remaining geometry-dependent term we solve the screened-Poisson diffusion problem directly inside the signed-distance domain with Walk on Spheres, without an interior mesh or a tangent half-space approximation; the estimator matches closed-form tests to 0.75%. Against a four-case path-traced benchmark it improves the back-lit, thickness-governed case but not every front-lit or curved case, showing that the method reduces geometry error within diffusion and does not replace radiative transport.
引用
@article{arxiv.2606.29387,
title = {Dipole Diffusion Error in Thin Geometry: Optical Thickness Laws for Grid-Free Subsurface Scattering},
author = {Faruk Alpay and Baris Basaran},
journal= {arXiv preprint arXiv:2606.29387},
year = {2026}
}
备注
22 pages, 13 figures, 1 table. Ancillary files include the full reproduction code (Python/NumPy CPU reference and Apple Metal GPU kernels) and all result data