CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
- URL: http://arxiv.org/abs/2609.07434v2
- Date: Sun, 13 Sep 2026 12:36:10 GMT
- Title: CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
- Abstract summary: 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.
- Score: 14.39975999842683
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.
Related papers
- ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints [63.53415461875466]
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.
arXiv Detail & Related papers (2026-08-25T15:58:33Z) - IterCAD: Iterative Program Repair for CAD Code Generation from Orthographic Views [35.452775950511935]
IterCAD is an iterative framework that reformulates orthographic-view-to-CAD generation as a progressive program repair process.<n>IterCAD repeatedly analyzes the current CAD result, reasons about its discrepancy with the target views, and explicitly decides whether to REVISE the code or STOP the refinement process.<n>IterCAD consistently improves code executability and geometric fidelity over strong one-shot baselines.
arXiv Detail & Related papers (2026-08-25T03:22:10Z) - 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) - BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD [14.284307846349774]
We present BenchCAD, a unified benchmark for industrial CAD reasoning.<n> BenchCAD contains 17,900 execution-verified CadQuery programs across 106 industrial part families.<n>It evaluates models through visual question answering, code question answering, image-to-code generation, and instruction-guided code editing.
arXiv Detail & Related papers (2026-05-11T17:13:36Z) - 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) - Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD [22.163726324892256]
We propose a hierarchical and geometry-aware graph as an intermediate representation.<n>Instead of mapping text directly to code, our framework first predicts structure and constraints, then conditions action sequencing and code generation.<n>Our method consistently outperforms existing approaches in both geometric fidelity and accurate satisfaction of geometric constraints.
arXiv Detail & Related papers (2026-04-11T07:49:16Z) - 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) - CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific Tokenization [16.26305802216836]
CAD-Tokenizer represents CAD data with modality-specific tokens using a sequence-based VQ-VAE with primitive-level pooling and constrained decoding.<n>This design produces compact, primitive-aware representations that align with CAD's structural nature.
arXiv Detail & Related papers (2025-09-25T13:38:36Z) - 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) - AutoCAD: Automatically Generating Counterfactuals for Mitigating
Shortcut Learning [70.70393006697383]
We present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
arXiv Detail & Related papers (2022-11-29T13:39:53Z)
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.