# Reconstruction of 3D Porous Media From 2D Slices

arXiv: https://arxiv.org/abs/1901.10233 · HTML: https://mlsanity.com/paper/1901.10233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Super-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation Accuracy](https://mlsanity.com/paper/2501.06939/) (2025)
- [3D Underwater Path Planning via Generative Flow Field Surrogates](https://mlsanity.com/paper/2606.06077/) (2026)

## Abstract (arXiv)

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices), we recover the three-dimensional structure around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method provides a good reconstruction in terms of Minkowski functionals.