ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints

• URL: http://arxiv.org/abs/2608.24760v1
• Date: Tue, 25 Aug 2026 15:58:33 GMT
• Title: ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
• Abstract summary: We argue that missing spatial constraints should be inferred with respect to the underlying construction structure.<n>We propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion.
• Score: 63.53415461875466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constraints required for valid CAD construction, a challenge that has been largely overlooked by existing methods. In this paper, we argue that missing spatial constraints should be inferred with respect to the underlying construction structure and informed by reusable design experience. Based on this insight, we propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion. ExpConCAD first recovers the intended construction structure and constraint scopes, then retrieves relevant constraint-completion experience for similar scopes to complete the missing spatial constraints, and finally generates executable CadQuery programs. Extensive experiments demonstrate the effectiveness of ExpConCAD and provide insights into the role of construction structure understanding and experience memory in spatial constraint completion. Our code is available at: https://github.com/Hotjiashell/ExpConCAD.

Related papers

• CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification [14.39975999842683]
  We propose a framework that makes design intent explicit and lets a system check generated code against that intent.<n>The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations.
  arXiv  Detail & Related papers  (2026-09-07T12:42:42Z)
• MIRAGE-CAD: Construction-Mediated Multimodal Generation of Executable CAD Programs [0.0]
  MIRAGE-CAD maps each input to a shared construction representation and mediates program generation through an explicit construction-plan interface.<n>On 2,500 held-out queries per modality, the system achieves 55.4-70.0% build success and 52.3-66.2% STEP export success without retrieval at inference.
  arXiv  Detail & Related papers  (2026-08-24T09:45:36Z)
• HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding [45.92807927638433]
  HierCAD is a hierarchical text-to-CAD framework that improves both structural reasoning and parameter prediction.<n>Our code is available at https://github.com/Collab-Gen/HierCAD.
  arXiv  Detail & Related papers  (2026-07-13T10:00:53Z)
• CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models [80.47087401394882]
  We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories.<n>We construct a dataset of 450k real-world CAD models spanning 15 modeling operations.<n>Fine-tuning a VLM on this representation yields state-of-the-art results in text-conditioned CAD generation and image-based reconstruction.
  arXiv  Detail & Related papers  (2026-05-03T15:12:36Z)
• ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation [24.959136090642758]
  Parametric Computer-Aided Design (CAD) of articulated assemblies is essential for product development.<n>We propose ArtiCAD, the first training-free multi-agent system capable of generating editable, articulated CAD assemblies directly from text or images.
  arXiv  Detail & Related papers  (2026-04-13T04:49:30Z)
• DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces [67.45867367326105]
  We propose DreamCAD, a multi-modal generative framework that directly produces editable BReps from point-level supervision.<n>DreamCAD represents each BRep as a set of parametric patches and uses a differentiable tessellation method to generate meshes.<n>DreamCAD achieves state-of-the-art performance on ABC and averse benchmarks across text, image, and point modalities.
  arXiv  Detail & Related papers  (2026-03-05T19:04:07Z)
• CADKnitter: Compositional CAD Generation from Text and Geometry Guidance [8.644079160190175]
  We propose CADKnitter, a compositional CAD generation framework with a geometry-guided diffusion sampling strategy.<n>CADKnitter is able to generate a complementary CAD part that follows both the geometric constraints of the given CAD model and the semantic constraints of the desired design text prompt.<n>We also curate a dataset, so-called KnitCAD, containing over 310,000 samples of CAD models, along with textual prompts and assembly metadata.
  arXiv  Detail & Related papers  (2025-12-12T01:06:38Z)
• HistCAD: Geometrically Constrained Parametric History-based CAD Dataset [7.7008607520955]
  HistCAD is a large-scale dataset featuring constraint-aware modeling sequences.<n>HistCAD provides a unified benchmark for advancing editable, constraint-aware, and semantically enriched generative CAD modeling.
  arXiv  Detail & Related papers  (2025-12-08T05:52:14Z)
• RoomCraft: Controllable and Complete 3D Indoor Scene Generation [51.19602078504066]
  RoomCraft is a multi-stage pipeline that converts real images, sketches, or text descriptions into coherent 3D indoor scenes.<n>Our approach combines a scene generation pipeline with a constraint-driven optimization framework.<n>RoomCraft significantly outperforms existing methods in generating realistic, semantically coherent, and visually appealing room layouts.
  arXiv  Detail & Related papers  (2025-06-27T15:03:17Z)
• CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning [31.342222156939403]
  We introduce CReFT-CAD, a two-stage fine-tuning paradigm that first employs a curriculum-driven reinforcement learning stage with difficulty-aware rewards to build reasoning ability steadily.<n>We release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning.
  arXiv  Detail & Related papers  (2025-05-31T13:52:56Z)
• CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images [69.7768227804928]
  CADCrafter is an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data.<n>We introduce a geometry encoder to accurately capture diverse geometric features.<n>Our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.
  arXiv  Detail & Related papers  (2025-04-07T06:01:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.