Automatic Extraction of Joint Orientations in Rock Mass Using PointNet and DBSCAN

Measurement of joint orientation is an essential task for rock mass discontinuity characterization. This work presents a methodology for automatic extraction of joint orientations in a rock mass from 3D point cloud data generated using Unmanned Aerial Vehicles and photogrammetry. Our algorithm first automatically classifies joints on 3D rock surface using state-of-the-art deep network architecture PointNet. It then identifies individual joints by the Density-Based Scan with Noise (DBSCAN) clustering and computes their orientations by fitting least-square planes using Random Sample Consensus. A major case study has been developed to evaluate the performance of the entire methodology. Our results showed the proposed approach outperforms similar approaches in the literature both in terms of accuracy and time complexity. Our experiments show the great potential in the application of 3D deep learning techniques for discontinuity characterization which might be used for the estimation of other parameters as well.