A Genetic Algorithm Based Heterogeneous Subsurface Scattering Representation (original) (raw)

GenSSS: a genetic algorithm for measured subsurface scattering representation

2021

We present a novel genetic algorithm-based approach for the compact representation of heterogeneous, optically thick, translucent materials. Utilizing genetic optimization, we also find the best transformation to represent measured subsurface scattering data. We employ a factored subsurface scattering representation, based on a singular value decomposition (SVD), separately applying the SVD per-color channel of the transformed profiles. In order to achieve a compact, accurate representation, we perform this iteratively on the model errors. By allowing the number of iterations to be customized, our representation provides a mechanism to trade the visual quality possible against the level of compression achieved through our representation. We validate our approach by analyzing a range of real-world translucent materials, geometries and lighting conditions. For heterogeneous translucent materials, we further demonstrate that for the same level of compression, our method achieves greater visual accuracy than alternative techniques. Finally, we present an application of our factored representation, which can be used to convert heterogeneous materials into homogeneous material representations.

A Compact Tucker-Based Factorization Model for Heterogeneous Subsurface Scattering

This paper presents a novel compact factored subsurface scattering representation for optically thick, heterogeneous translucent materials. Our subsurface scattering representation is a combination of Tucker-based factorization and a linear regression method. We first apply Tucker factorization on the intensity profiles of the heterogeneous subsurface scattering responses. Next, we fit a polynomial model for characterizing the differences between the different color channels with a linear regression procedure. We show that our method achieves good compression while maintaining visual fidelity. We validate our heterogeneous subsurface scattering representation on various real-world heterogeneous translucent materials, geometries and lighting conditions

Efficient estimation of spatially varying subsurface scattering parameters

2006

Abstract We present an image-based technique to efficiently acquire spatially varying subsurface reflectance properties of a human face. The estimated properties can be used directly to render faces with spatially varying scattering, or can be used to estimate a robust average across the face. We demonstrate our technique with renderings of peoples' faces under novel, spatially-varying illumination and provide comparisons with current techniques.

An Efficient Plugin for Representing Heterogeneous Translucent Materials

This paper presents a plugin that adds an efficient representation of heterogeneous translucent materials to the Blender 3D modeling tool. Algorithm of the plugin is based on Singular Value Decomposition (SVD) method and Mitsuba renderer is the default rendering software used by the proposed plugin. We validate the efficiency of the proposed plugin by using a set of measured heterogeneous subsurface scattering data sets.

Separable Subsurface Scattering

Real-time results of our method for simulating translucent materials (skin on the left, ketchup on the right). Our separable subsurface-scattering method enables the generation of these images using only two convolutions (versus 12 in the sum-of-Gaussians approach [dLE07, JSG09]) and seven samples per pixel, while featuring quality comparable with the current state of the art, at a fraction of its cost. It can be implemented as a post-processing step and takes only 0.489 ms per frame on an AMD Radeon HD 7970 at 1080p, which makes it highly suitable for challenging real-time scenarios. Abstract In this paper we propose two real-time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 milliseconds per frame to execute. This makes them a practical option for real-time production scenarios. Current state-of-the-art, real-time approaches simulate subsurface light transport by approximating the radially symmetric non-separable diffusion kernel with a sum of separable Gaussians, which requires multiple (up to twelve) 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low-rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: the first one yields a high-quality diffusion simulation, while the second one offers an attractive trade-off between physical accuracy and artistic control. Both allow rendering subsurface scattering using only two 1D convolutions, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost. Using our importance-sampling and jittering strategies, only seven samples per pixel are required. Our methods can be implemented as simple post-processing steps without intrusive changes to existing rendering pipelines.