As the digitalization process advances in several industries, the creation of 3D digital models is becoming more and more required. Commonly, these models are constructed by 3D designers, requiring considerable manual effort when the modeled object is complex. In addition, since the designer does not have an accurate reference in most cases, the resulting model is prone to measurement errors. However, it is possible to minimize the construction effort and the measurement error by using 3D acquisition techniques and previously constructed CAD models. The typical output of a 3D acquisition technique is a raw 3D point cloud, which needs processing to reduce the inherent noise and lack of topological information. CAD models are typically used to document an engineering design process, presenting high complexity and too many details irrelevant to many visualization processes. So, depending on the application, we must severely simplify the CAD model to meet its requirements. In this thesis, we focus on the construction of 3D digital models from these sources. More precisely, we present a set of geometry processing algorithms to automatize different stages of a typical workflow used for this construction. First, we present a point cloud denoising algorithm that seeks to preserve the sharp features of the underlying surface. This algorithm includes solutions for the normal estimation and sharp feature detection problems. Second, we present an extension of the point cloud denoising algorithm to process triangle meshes, where we take advantage of the explicit topology defined by the mesh. Finally, we present an algorithm for the extreme simplification of complex CAD models, which tends to approximate the outer surface of the modeled object. The proposed algorithms are compared with state-of-the-art methods, showing competitive results and outperforming them in most test cases.