## 2 Related Work
### Solid Feature Reconstruction  
In the field of reverse engineering, triangular mesh models reconstructed from unstructured data are no longer adequate for the requirements of product parametric design and variant design. It is essential that a reconstructed 3D model includes solid CAD features that precisely convey forward design information through a process referred to as solid feature reconstruction. In recent years, [PERSON] and [PERSON] (2020) proposed a methodology for reconstructing a solid model from a 3D point cloud. Initially, the target object's shape is reconstructed by iteratively merging multiple input point clouds into a watertight triangular mesh. Subsequent to this, mesh segmentation is employed to isolate individual geometric feature surfaces from the triangular mesh. Eventually, the manifold topology, which delineates the connectivity between different geometric surfaces, is automatically derived, and a mathematical representation of each geometric feature is generated. The accuracy of the 3D mesh edges significantly impacts the efficacy of this method, consequently constraining the complexity of the reconstructed CAD model. [PERSON] et al. (2020) introduced a comprehensive approach for reconstructing a solid model from a 3D mesh, comprising three sequential steps: primitive extraction, wire construction, and solid creation. Initially, the matching geometric primitive (such as a plane, sphere, cylinder, or cone) corresponding locally to the 3D mesh of interest is determined by analyzing differential geometry operators. Subsequently, the relationships among all the extracted geometric primitives are established to calculate the intersection curves between pairs of these primitives. These curves are then amalgamated to construct a continuous wire. Subsequently, a consistent solid CAD model is composed by amalgamating the information extracted or reconstructed throughout the prior two steps. [PERSON] et al. (2020) focused on the comprehensive and detailed shape analysis of a triangular mesh. They explored the utilization of protruding section sets (PCSs) to highlight model features. Additionally, they introduced a linear embedded algorithm (LLE) for examining these protruding sections based on cross-sectional geometric properties. Subsequently, they developed a solid model and introduced an affinity propagation algorithm (AP) to cluster these protruding sections, forming distinct scanning parts by propagating constraint relationships among them. [PERSON] et al. (2020) presented a method for transforming a triangular mesh model into a solid model. Initially, preprocessing is utilized to eliminate noise from the input mesh, followed by mesh segmentation to extract distinct geometric feature blocks. Through tailored approaches for entity and surface features, the original characteristics are reconstructed from the segmented feature blocks. Subsequently, Booleanor trimming operations are employed on both the entity and surface features to shape the final solid model. Subsequently, [PERSON] et al. [27] focused on the triangular mesh as their research subject. They delved into techniques for extracting characteristic parameters related to four types of features, namely extrusion, rotation, sweep, and loft, and analyzed the two-dimensional cross-sectional profiles of these features using piecewise fitting and global optimization methods. [PERSON] et al. [28; 29] presented an innovative technique for converting an open triangular mesh, with its perimeter bounded by a set of smooth curves, into a T-NURCC surface that smoothly approximates both the mesh and curve geometry. Initially, a transfinite interpolation construction process is utilized to define a \(C^{0}\) patch network that interpolates the curves at the boundaries and coincides with the mesh internally. Following that, an approximation algorithm is implemented to transform the network into a T-NURCC surface. Finally, the resulting T-NURCCs with adjacent faces are integrated to form a watertight, low-tolerance solid CAD model.