HistCAD is an executable representation and dataset comprising 180,495 parametric construction histories with sketch constraints and feature operations, split into Academic and Industrial collections. The benchmark evaluates whether predicted constraints can enable a history to reproduce an initial model and perform a specified dimensional edit, measuring success when the model matches the required geometry, reaches the target value, preserves relations, and rebuilds through the entire history. Predictors trained on both collections achieve 52.4% edit success on Academic data and 29.0% on Industrial data, with models retaining only endpoint-connectivity constraints sometimes reaching the target but failing to preserve all relations.
arXiv:2609.22688v2 Announce Type: replace-cross Abstract: Parametric CAD modeling requires not only matching the target shape, but also preserving design intent after parameter editing. However, most...
arXiv:2609.22688v1 Announce Type: new Abstract: Generating parametric CAD models requires accurate geometry and stable feature dependencies. Existing methods face challenges in selecting geometric re...
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.
AgenticCADedit introduces a stateful, tool‑mediated approach to multimodal 3D CAD editing, transforming the process from generating a single complete program to executing a sequence of incremental, verifiable actions on a persistent CAD state. By committing each step, inspecting geometry, and selectively reverting faulty operations, the method preserves partial progress and builds upon earlier edits. Experiments across three large language models show substantial gains in validity and acceptance, with the weakest baseline model’s validity rising from 51.0% to 94.8% and a token‑cost reduction of 66.7% compared to neuralCAD‑Edit.
arXiv:2609.07434v1 Announce Type: new Abstract: Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large languag...
CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.
Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task. Existing methods can propose CAD operations, but no single source of proposals works equally well across di...
arXiv:2606. 30429v1 Announce Type: new Abstract: Text-to-3D systems can now synthesize a mechanical part from a single sentence, yet the result is a shape to render, not a design to edit.
arXiv:2605. 10873v2 Announce Type: replace-cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented across datasets, modalities, and metrics.
arXiv:2610.15681v1 Announce Type: cross Abstract: Parametric Computer-Aided-Design (CAD) modeling from human intent remains challenging, particularly during the conceptual design stage, where design...
arXiv:2608. 09296v1 Announce Type: new Abstract: A CAD model is not engineering-grade merely because it looks correct.
IterCAD introduces an iterative program‑repair framework for generating executable parametric CAD code from dimension‑annotated orthographic drawings. Unlike one‑shot vision‑language models, IterCAD repeatedly evaluates the current CAD output, identifies discrepancies with the target views, and decides whether to revise the code or stop refining. The authors build a structured revise‑or‑stop dataset (IterCAD‑RS) and a three‑stage training pipeline—initial generation, revision learning, and multi‑turn reinforcement learning—to enable this progressive correction, achieving higher code executability and geometric fidelity on the CADExpert benchmark.