Weier, PhilippeDroske, MarcHanika, JohannesWeidlich, AndreaVorba, JiríBousseau, Adrien and McGuire, Morgan2021-07-122021-07-1220211467-8659https://doi.org/10.1111/cgf.14347https://diglib.eg.org:443/handle/10.1111/cgf14347We present Optimised Path Space Regularisation (OPSR), a novel regularisation technique for forward path tracing algorithms. Our regularisation controls the amount of roughness added to materials depending on the type of sampled paths and trades a small error in the estimator for a drastic reduction of variance in difficult paths, including indirectly visible caustics. We formulate the problem as a joint bias-variance minimisation problem and use differentiable rendering to optimise our model. The learnt parameters generalise to a large variety of scenes irrespective of their geometric complexity. The regularisation added to the underlying light transport algorithm naturally allows us to handle the problem of near-specular and glossy path chains robustly. Our method consistently improves the convergence of path tracing estimators, including state-of-the-art path guiding techniques where it enables finding otherwise hard-to-sample paths and thus, in turn, can significantly speed up the learning of guiding distributions.Computing methodologiesRendering10.1111/cgf.14347139-151