Cold-formed steel (CFS) construction provides efficient, affordable, and resilient building systems that meet both domestic and international demands. The most significant advantages of cold-formed construction can be listed as the high strength-to-weight ratio (increased structural efficiency and economy in production, transportation, and handling by reducing labor costs and worker fatigue); weather and rot-resistant galvanized members; and rapid construction due to repetitive framing and modular-style walls. These listed qualities make CFS construction especially important for earthquake-prone regions where light, durable and resilient structures are required. The CFS research has evolved significantly during the last two decades with the introduction of detailed design and construction guidelines. However, there are still many research questions regarding as-is geometrical integrity and its effect on member behavior. Currently, in order to predict the physical response of CFS members, the geometrical properties of the input selection is used. Since the member thickness to dimension ratio is significantly low, any existing geometrical imperfection may result in significant changes in member behavior. In order to investigate this topic further, a novel strategy, which aims to capture geometric imperfections on CFS members by using 3D images, is developed. These 3D images are generated from high-definition images captured by a standard camera. Several feature extraction and matching techniques are used to perform 3D image construction. Both cross-section change detection and member skeleton tracking methods are used on the investigated CFS members for locating and quantifying the geometrical imperfections. The extracted geometric imperfection information is also compared with micrometer measurements in order to assess the accuracy of the developed strategy. The main purpose of this research is to enhance the numerical modeling methodologies by fully representing CFS members’ geometric imperfections.