# A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud

arXiv: https://arxiv.org/abs/1707.03266 · HTML: https://mlsanity.com/paper/1707.03266/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

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

- [LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation](https://mlsanity.com/paper/2103.09160/) (2021)
- [A review of laser scanning for geological and geotechnical applications in underground mining](https://mlsanity.com/paper/2211.11181/) (2022)
- [EPCS: Endpoint-based Part-aware Curve Skeleton Extraction for Low-quality Point Clouds](https://mlsanity.com/paper/2211.09488/) (2022)
- [Towards Integrated Rock Support Visualisation in 3D Point Cloud of Underground Mines](https://mlsanity.com/paper/2605.20973/) (2026)
- [Automated Discontinuity Set Characterisation in Enclosed Rock Face Point Clouds Using Single-Shot Filtering and Cyclic Orientation Transformation](https://mlsanity.com/paper/2602.01783/) (2026)

## Abstract (arXiv)

Conventional manual surveys of rock mass fractures usually require large amounts of time and labor; yet, they provide a relatively small set of data that cannot be considered representative of the study region. Terrestrial laser scanners are increasingly used for fracture surveys because they can efficiently acquire large area, high-resolution, three-dimensional (3D) point clouds from outcrops. However, extracting fractures and other planar surfaces from 3D outcrop point clouds is still a challenging task. No method has been reported that can be used to automatically extract the full extent of every individual fracture from a 3D outcrop point cloud. In this study, we propose a method using a region-growing approach to address this problem; the method also estimates the orientation of each fracture. In this method, criteria based on the local surface normal and curvature of the point cloud are used to initiate and control the growth of the fracture region. In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy. Compared with manually acquired field survey data, our method obtained better-quality fracture data, thereby demonstrating the high potential utility of the proposed method.