Measurement of joint orientation is an essential task for rock mass disconti-nuity characterization. This work presents a methodology for automatic ex-traction of joint orientations in a rock mass from 3D point cloud data gener-ated 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 Con-sensus. A major case study has been developed to evaluate the performance of the entire methodology. Our results showed the proposed approach out-performs similar approaches in the literature both in terms of accuracy and time complexity. Our experiments show the great potential in the applica-tion of 3D deep learning techniques for discontinuity characterization which might be used for the estimation of other parameters as well.