# GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation

arXiv: https://arxiv.org/abs/2401.15245 · HTML: https://mlsanity.com/paper/2401.15245/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Hyper-Realist Rendering: A Theoretical Framework](https://mlsanity.com/paper/2401.12853/) (2024)

## Built on by (most cited first, 5 of 5)

- [MelNet: A Real-Time Deep Learning Algorithm for Object Detection](https://mlsanity.com/paper/2401.17972/) (2024)
- [ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images](https://mlsanity.com/paper/2402.02246/) (2024)
- [LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection](https://mlsanity.com/paper/2411.11826/) (2024)
- [OkanNet: A Lightweight Deep Learning Architecture for Classification of Brain Tumor from MRI Images](https://mlsanity.com/paper/2604.01264/) (2026)
- [TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games](https://mlsanity.com/paper/2610.10061/) (2026)

## Abstract (arXiv)

This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using Mitsuba renderer, which is an open source rendering software. The proposed plugin has been validated on measured subsurface scattering data. It's shown that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects, accurately, compactly and computationally efficiently.