Meshless Monte Carlo Radiation Transfer Method for Curved Geometries using Signed Distance Functions

TL;DR

The paper introduces signedMCRT, a novel meshless Monte Carlo radiation transfer method using signed distance functions, which accurately models curved geometries more efficiently than traditional voxel-based approaches.

Contribution

It presents a new geometry representation using signed distance functions for Monte Carlo radiation transfer, improving accuracy and speed over existing voxel-based methods.

Findings

sMCRT is up to 45 times more accurate than voxel-based methods.

sMCRT can be three times faster than voxel models for similar scenarios.

Validated against theoretical and other voxel-based codes.

Abstract

Significance: Monte Carlo radiation transfer (MCRT) is the gold standard of modeling light transport in turbid media. Typical MCRT models use voxels or meshes to approximate experimental geometry. A voxel based geometry does not allow for the accurate modeling of smooth curved surfaces, such as may be found in biological systems or food and drink packaging. Aim: We present our algorithm which we term signedMCRT (sMCRT), a new geometry-based method which uses signed distance functions (SDF) to represent the geometry of the model. SDFs are capable of modeling smooth curved surfaces accurately whilst also modeling complex geometries. Approach: We show that using SDFs to represent the problem's geometry is more accurate and can be faster than voxel based methods. sMCRT, can easily be incorporated into existing voxel based models. Results: sMCRT is validated against theoretical expressions, and other voxel based MCRT codes. We show that sMCRT can accurately model arbitrary complex geometries such as microvascular vessel network using SDFs. In comparison to the current state-of-the-art in MCRT methods specifically for curved surfaces, sMCRT is up-to forty-five times more accurate. Conclusions: sMCRT is a highly accurate, fast MCRT method that outperforms comparable voxel based models due to its ability to model smooth curved surfaces. sMCRT is up-to three times faster than a voxel model for equivalent scenarios. sMCRT is publicly available at https://github.com/lewisfish/signedMCRT