MidSurfNet is a learning-based method for pairing faces in thin‑walled CAD models, addressing the key subproblem of mid‑surface abstraction for finite element analysis. It uses a dual‑stream scorer—one geometry stream and one attributed‑topology stream—fused by a pair‑conditioned gate to make per‑pair decisions that replace manual thresholds. The method organizes retained relations into variable‑cardinality face groups independent of processing order, and on a new MidSurf dataset of 1,575 annotated models it achieves an 87.32% pair‑level F1‑score and a 75.42% completion rate, outperforming rule‑based baselines, especially on multi‑wall‑thickness cases.
arXiv:2606. 01891v1 Announce Type: cross Abstract: Mid-surface abstraction is essential for finite element analysis of thin-walled CAD models.
arXiv:2607. 20642v1 Announce Type: cross Abstract: Computer aided design (CAD) is ubiquitous: virtually any modern object was designed using editable CAD tools.
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
arXiv:2609.37139v1 Announce Type: new Abstract: 3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remain...
arXiv:2606. 17696v1 Announce Type: new Abstract: Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited.
arXiv:2607. 05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications.
arXiv:2606. 08287v1 Announce Type: new Abstract: Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios.
arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.
Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise explicit surface representations, often by converting radiance fields into meshes and training heavily on surface-supervised data. We instead fine-tune a pretrained 3D-aware generative model directly from a learned reward over radiance-field density ($σ$) values, with no externally supplied mesh or shape prior.
arXiv:2606. 01702v1 Announce Type: cross Abstract: Deep learning in computer-aided design (CAD) remains fundamentally constrained by the data scarcity challenge: authentic CAD data is difficult to collect at scale, while synthetic data may not faithfully reflect real design practice.
arXiv:2608.00800v2 Announce Type: replace Abstract: Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries u...
arXiv:2607.05568v2 Announce Type: replace-cross Abstract: Compact primitive abstractions represent 3D shapes with a few geometric primitives while preserving recognizable components. Learned methods...