# Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy

arXiv: https://arxiv.org/abs/2505.18664 · HTML: https://mlsanity.com/paper/2505.18664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 11 of 11)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [OctNet: Learning Deep 3D Representations at High Resolutions](https://mlsanity.com/paper/1611.05009/) (2016)
- [Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs](https://mlsanity.com/paper/1703.09438/) (2017)
- [Learning shape correspondence with anisotropic convolutional neural networks](https://mlsanity.com/paper/1605.06437/) (2016)
- [Sparse 3D convolutional neural networks](https://mlsanity.com/paper/1505.02890/) (2015)
- [GIFT: A Real-time and Scalable 3D Shape Search Engine](https://mlsanity.com/paper/1604.01879/) (2016)
- [Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks](https://mlsanity.com/paper/2110.11281/) (2021)

## Abstract (arXiv)

We present a memory-efficient algorithm for significantly enhancing the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks using a generative model. The proposed model achieves a 16x increase in resolution and corrects inaccuracies in segmentation caused by the overlapping X-ray attenuation in micro-CT measurements across different minerals. The generative model employed is a 3D Octree-based convolutional Wasserstein generative adversarial network with gradient penalty. To address the challenge of high memory consumption inherent in standard 3D convolutional layers, we implemented an Octree structure within the 3D progressive growing generator model. This enabled the use of memory-efficient 3D Octree-based convolutional layers. The approach is pivotal in overcoming the long-standing memory bottleneck in volumetric deep learning, making it possible to reach 16x super-resolution in 3D, a scale that is challenging to attain due to cubic memory scaling. For training, we utilized segmented 3D low-resolution micro-CT images along with unpaired segmented complementary 2D high-resolution laser scanning microscope images. Post-training, resolution improved from 7 to 0.44 micro-m/voxel with accurate segmentation of constituent minerals. Validated on Berea sandstone, this framework demonstrates substantial improvements in pore characterization and mineral differentiation, offering a robust solution to one of the primary computational limitations in modern geoscientific imaging.