Clarifying the Concepts and Terminologies for representation of AI-Driven Parametric CAD Datasets

A CAD model can be encoded either as an explicit geometric representation, such as a point cloud, a mesh, or a boundary representation (B-Rep), or as an implicit procedural representation of its modeling procedure. Explicit geometric representations primarily support tasks such as 3D visual recognition, segmentation, classification, visualization, rendering, analysis, and machining. However, they capture the geometric shape and visual characteristics of a CAD model rather than its design procedure. Implicit representations may encode either simple Boolean operations procedure (solid modeling, simple sketch-extrude) or feature-based modeling procedure (parametric feature-based modeling, which involves sketches, constraints, feature trees, topological naming, and selection mechanisms). Because the “solid modeling” lacks the core component for parametric modeling, editing and reuse of a CAD model still demand substantial manual effort in industrial scenarios, limiting its ability to support rapid product development in globally competitive markets. Consequently, industrial-grade CAD paradigm has progressively evolved from “solid modeling” to “parametric feature-based modeling.” The latter has become the dominant CAD paradigm in contemporary industrial CAD applications, including CATIA, Inventor, Onshape, SolidWorks, Creo, NX and so on. Dataset is the foundation of the AI-driven CAD. Based on two milestone books in the development history of CAD area, this paper clarifies the concepts and terminologies, which are conflated between DeepCAD dataset (the first publicly available dataset for solid modeling) and WHUCAD dataset (the first publicly available dataset for parametric feature-based modeling). Our clarification aligns with the theory researches of CAD area as well as the real-world industrial CAD applications.