DIP-CAD: A Design-Intent-Preserving Agent Harness for Parametric CAD Modeling
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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...
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: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...
CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.
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: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.