CADReasoner: Iterative Program Editing for CAD Reverse Engineering

• URL: http://arxiv.org/abs/2603.29847v1
• Date: Wed, 18 Feb 2026 10:27:29 GMT
• Title: CADReasoner: Iterative Program Editing for CAD Reverse Engineering
• Abstract summary: Many AI systems tackle CAD reverse engineering, but most are single-pass and miss fine geometric details.<n>We introduce CADReasoner, a model trained to iteratively refine its prediction using geometric discrepancy.<n>The model outputs a runnable CadQuery Python program whose rendered mesh is fed back at the next step.
• Score: 8.468103398438917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer-Aided Design (CAD) powers modern engineering, yet producing high-quality parts still demands substantial expert effort. Many AI systems tackle CAD reverse engineering, but most are single-pass and miss fine geometric details. In contrast, human engineers compare the input shape with the reconstruction and iteratively modify the design based on remaining discrepancies. Agent-based methods mimic this loop with frozen VLMs, but weak 3D grounding of current foundation models limits reliability and efficiency. We introduce CADReasoner, a model trained to iteratively refine its prediction using geometric discrepancy between the input and the predicted shape. The model outputs a runnable CadQuery Python program whose rendered mesh is fed back at the next step. CADReasoner fuses multi-view renders and point clouds as complementary modalities. To bridge the realism gap, we propose a scan-simulation protocol applied during both training and evaluation. Across DeepCAD, Fusion 360, and MCB benchmarks, CADReasoner attains state-of-the-art results on clean and scan-sim tracks.

Related papers

• CADENA: Stepwise CAD Reverse Engineering [7.593370103613143]
  We introduce CADENA, a model that reconstructs a 3D mesh as a parametric CAD program.<n>We also introduce CADENA-Bench, a benchmark that measures performance across categories of mechanical parts.
  arXiv  Detail & Related papers  (2026-08-01T17:59:58Z)
• Autoregressive B-Rep Shape Generation with Parametric Surfaces [83.03418080213359]
  ParaCAD is an autoregressive framework for point-cloud-conditioned B-Rep generation.<n>Our model first generates parametric surfaces with constrained UV domains, and then constructs a valid B-Rep by globally intersecting these surfaces.<n>Extensive experiments demonstrate that ParaCAD produces accurate B-Reps with faithful point-cloud alignment.
  arXiv  Detail & Related papers  (2026-07-19T06:14:50Z)
• IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing [31.86100517851177]
  IterCAD is a unified multimodal agent framework for closed-loop, interactive CAD generation and editing.<n>It achieves highly competitive performance across multiple benchmarks, significantly outperforming existing approaches in both code executability and geometric precision.
  arXiv  Detail & Related papers  (2026-06-11T13:53:21Z)
• GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback [16.42151644490948]
  We introduce GIFT, a framework that leverages geometric feedback to turn test-time compute into a bootstrapped set of high-quality training samples.<n>It improves mean IoU by 12% over a strong supervised baseline and remains competitive with more complex multimodal systems.
  arXiv  Detail & Related papers  (2026-03-28T23:49:20Z)
• MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans [22.243878630903577]
  We introduce a novel approach to CAD reverse engineering inspired by how human designers manually perform the task.<n>Our method leverages multi-plane cross-sections to extract 2D patterns and capture fine parametric details more effectively.<n>It enables the reconstruction of detailed and editable CAD models, outperforming state-of-the-art methods and, for the first time, incorporating sketch constraints directly into the reconstruction process.
  arXiv  Detail & Related papers  (2025-10-27T15:33:51Z)
• GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing [3.5539239472975583]
  We introduce GenCAD-3D, a multimodal generative framework to generate 3D CAD programs.<n>We also present SynthBal, a synthetic data augmentation strategy designed to balance and expand datasets.<n>Our experiments show that SynthBal significantly boosts reconstruction accuracy, reduces the generation of invalid CAD models, and markedly improves performance on high-complexity geometries.
  arXiv  Detail & Related papers  (2025-09-17T19:10:44Z)
• From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation [47.67703214044401]
  We propose CAD-RL, a multimodal Chain-of-Thought guided reinforcement learning framework for CAD modeling code generation.<n>Our method combines Cold Start with goal-driven reinforcement learning post training using three task-specific rewards.<n>Experiments demonstrate that CAD-RL achieves significant improvements in reasoning quality, output precision, and code executability.
  arXiv  Detail & Related papers  (2025-08-13T18:30:49Z)
• 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)
• cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning [55.16668009268005]
  We propose a multi-modal CAD reconstruction model that simultaneously processes all three input modalities.<n>Inspired by large language model (LLM) training paradigms, we adopt a two-stage pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated data, followed by reinforcement learning (RL) fine-tuning using online feedback, obtained programatically.<n>In the DeepCAD benchmark, our SFT model outperforms existing single-modal approaches in all three input modalities simultaneously.
  arXiv  Detail & Related papers  (2025-05-28T22:32:31Z)
• Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek [19.441404313543227]
  This study is the first investigation to incorporate both visual and Chain-of-Thought (CoT) feedback within the self-refinement mechanism for generating CAD models.<n>We present an innovative 3D CAD model dataset structured around the SSR (Sketch, Sketch-based feature, and Refinements) triple design paradigm.
  arXiv  Detail & Related papers  (2025-05-23T10:11:19Z)
• 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)
• PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction [86.726941702182]
  We introduce geometric guidance into the reconstruction network PS-CAD. We provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces.
  arXiv  Detail & Related papers  (2024-05-24T03:43:55Z)
• Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds [26.10631058349939]
  We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models. We also propose a novel implicit neural representation of freeform surfaces, driving up the performance of our overall CAD reconstruction scheme.
  arXiv  Detail & Related papers  (2023-12-07T08:23:44Z)
• 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.