Integrating artificial intelligence (AI) into computer-aided design (CAD) has shown the potential to transform design and manufacturing processes, enabling more efficient, intuitive, and intelligent workflows. In recent years, the application of AI to 3D CAD model generation tasks has gradually emerged. To better enable researchers to understand the current research status of the AI-based CAD generation field and to inspire them to conduct further research, this survey explores the role of AI in 3D CAD model generation tasks that utilize various representations and conditions, ranging from traditional machine learning to LLM-based approaches. Additionally, AI applications in other extended CAD areas are also touched upon in the survey. Finally, we analyze current progress, identify challenges and limitations faced by this field, and propose possible directions for future work.

We propose a novel method to compute globally injective parameterizations with arbitrary positional constraints on disk topology meshes. Central to this method is the use of a scaffold mesh that reduces the globally injective constraint to a locally flip-free condition. Hence, given an initial parameterized mesh containing flipped triangles and satisfying the positional constraints, we only need to remove the flips of a overall mesh consisting of the parameterized mesh and the scaffold mesh while always meeting positional constraints. To successfully apply this idea, we develop two key techniques. Firstly, an initialization method is used to generate a valid scaffold mesh and mitigate difficulties in eliminating flips. Secondly, edge-based remeshing is used to optimize the regularity of the scaffold mesh containing flips, thereby improving practical robustness. Compared to state-of-the-art methods,our method is much more robust. We demonstratethe capability and feasibility of our method on a large number of complex meshes.

A geometric mapping establishes a correspondence between two domains. Since no real object has zero or negative volume, such a mapping is required to be inversion-free. Computing inversion-free mappings is a fundamental task in numerous computer graphics and geometric processing applications, such as deformation, texture mapping, mesh generation, and others. This task is usually formulated as a non-convex, nonlinear, constrained optimization problem. Various methods have been developed to solve this optimization problem. As well as being inversion-free, different applications have various further requirements. We expand the discussion in two directions to (i) problems imposing specific constraints and (ii) combinatorial problems. This report provides a systematic overview of inversion-free mapping construction, a detailed discussion of the construction methods, including their strengths and weaknesses, and a description of open problems in this research field.