This study takes rock masses at the Songta Hydropower Station in Southeast China as examples to perform three-dimensional (3D) discrete fracture network (DFN) modeling. Field fracture traces are collected from four adits (PD252, PD254, PD262, and PD264) with 1.6 m high sampling windows. The relationship between 3D fracture parameters and two-dimensional collected trace information is investigated using data collected from short sampling windows. On the basis of this relationship, a simplified method is proposed to determine the 3D fracture density and disc diameter and generate 3D DFN. This method avoids the complex deduction processes of geometric fracture parameters, thereby simplifying 3D DFN modeling considerably. Although the method is developed on the basis of short-window collection, traditional data collected within a tall window are still applicable through the window dividing method. Short windows do not contain competent traces that reflect the structural information of rock masses. Therefore, errors are easily found during 3D DFN modeling. We propose a method to evaluate and reduce uncertainty effects. The uncertainties of fracture sets 1 and 2 are analyzed by increasing the sampling size in a single structural domain, and the errors are acceptable.