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Gaole Pan

Publications and source records attributed to Gaole Pan.

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A Micro-Ellipsoid Model for Wet Porous Materials Rendering

Wet porous materials, like wet ground, moist walls, or wet cloth, are common in the real world. These materials consist of transmittable particles surrounded by liquid, where the individual particle is invisible in the macroscopic view. While modeling wet porous materials is critical for various applications, a physically based model for wet porous materials is still absent. In this paper, we model these appearances in the media domain by extending the anisotropic radiative transfer equation to model porosity and saturation. Then, we introduce a novel particle model -- micro-ellipsoid -- by treating each particle as a transmittable ellipsoid, analogous to a micro-flake, to statistically characterize the overall optical behavior of the medium. This way, the foundational theory for media with porosity and saturation is established. Building upon this new medium, we further propose a practical bidirectional scattering distribution function (BSDF) model within the position-free framework--WetSpongeCake. As a result, our WetSpongeCake model is able to represent various appearances of wet porous materials using physical parameters (e.g., porosity and saturation), allowing both reflection and transmission. We validated our model through several examples: a piece of wet cloth, sand saturated with different liquids, or damp sculptures, demonstrating its ability to match real-world appearances closely.

cs.GR

Multiple-bounce Smith Microfacet BRDFs using the Invariance Principle

Smith microfacet models are widely used in computer graphics to represent materials. Traditional microfacet models do not consider the multiple bounces on microgeometries, leading to visible energy missing, especially on rough surfaces. Later, as the equivalence between the microfacets and volume has been revealed, random walk solutions have been proposed to introduce multiple bounces, but at the cost of high variance. Recently, the position-free property has been introduced into the multiple-bounce model, resulting in much less noise, but also bias or a complex derivation. In this paper, we propose a simple way to derive the multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) using the invariance principle. At the core of our model is a shadowing-masking function for a path consisting of direction collections, rather than separated bounces. Our model ensures unbiasedness and can produce less noise compared to the previous work with equal time, thanks to the simple formulation. Furthermore, we also propose a novel probability density function (PDF) for BRDF multiple importance sampling, which has a better match with the multiple-bounce BRDFs, producing less noise than previous naive approximations.

cs.GR