Rock quality designation (RQD) index, provides a general indication of rock mass quality and is widely used in many rock mass classification systems. From the literature review, it is concluded that the RQD methodology has several limitations and one of them is highlighted in this paper with the help of stochastic analysis with Monte Carlo simulation. The purpose of this paper is to improve the results of the RQD index through volumetric joint count (Jv) from accessible surface areas or when drill cores are not available. In this paper, we introduce a low-cost photogrammetric method for rock slope reconstruction with scaled and oriented 3D point cloud, ideally suited for geomechanical analysis. As an outcome, the 3D point cloud is then used to detect the discontinuity sets and to, define their orientation, normal spacing and persistence. Results are then used to calculate the number of joints per m3, which is then used as input in the empirical correlation between the RQD index and the Jv. The main advantage of the proposed methodology is that it is completely based on open-source software.