UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD

• URL: http://arxiv.org/abs/2606.05058v1
• Date: Wed, 03 Jun 2026 16:20:57 GMT
• Title: UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
• Abstract summary: We introduce UniCAD, a comprehensive benchmark for multi-modal CAD learning.<n>We present UniCAD-MLLM, a universal large language model that ingests text, images, sketches, and point clouds.<n>We will release the dataset, code, and pretrained models to accelerate future research.
• Score: 31.34982007288203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in isolation, and multi-modal, multi-task learning for CAD is hindered by the absence of a unified benchmark. To address this gap, we introduce UniCAD, a comprehensive benchmark for multi-modal CAD learning that covers point-to-CAD reconstruction, text/image-to-CAD generation, and CAD question answering across diverse input modalities. Alongside the benchmark, we present UniCAD-MLLM, a universal multi-modal large language model that ingests text, images, sketches, and point clouds and performs these heterogeneous tasks in an end-to-end fashion within a single framework. Extensive experiments on the UniCAD and Fusion360 benchmarks demonstrate that UniCAD-MLLM achieves state-of-the-art performance across all tasks, outperforming existing task-specific and multi-task baselines. We will release the dataset, code, and pretrained models to accelerate future research.

Related papers

• CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation [3.6136995620089145]
  We introduce CADBench, a unified benchmark for multimodal CAD program generation.<n>CADBench contains 18,000 evaluation samples spanning six benchmark families.<n>We benchmark eleven CAD-specialized and general-purpose vision-language systems, generating more than 1.4 million CAD programs.
  arXiv  Detail & Related papers  (2026-05-11T17:25:47Z)
• 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)
• 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)
• 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)
• CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction [11.33947758511237]
  We introduce the CAD review task to automatically detect and correct potential errors.<n>In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors.<n>We create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task.
  arXiv  Detail & Related papers  (2025-05-28T12:41:00Z)
• UniCAD: Efficient and Extendable Architecture for Multi-Task Computer-Aided Diagnosis System [48.83716673786449]
  We propose UniCAD, a unified architecture that seamlessly handles both 2D and 3D medical images.<n>A low-rank adaptation strategy is employed to adapt a pre-trained visual model to the medical image domain, achieving performance on par with fully fine-tuned counterparts.<n>Building on this unified CAD architecture, we establish an open-source platform where researchers can share and access lightweight CAD experts.
  arXiv  Detail & Related papers  (2025-05-14T06:21:27Z)
• 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)
• CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers [20.440350517523314]
  CAD-Assistant addresses user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software.<n>We consider a wide range of CAD-specific tools including a sketch image parameterizer, rendering modules, a 2D cross-section generator, and other specialized routines.
  arXiv  Detail & Related papers  (2024-12-18T12:57:56Z)
• CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM [39.113795259823476]
  We introduce the CAD-MLLM, the first system capable of generating parametric CAD models conditioned on the multimodal input.<n>We use advanced large language models (LLMs) to align the feature space across diverse multi-modalities data and CAD models' vectorized representations.<n>Our resulting dataset, named Omni-CAD, is the first multimodal CAD dataset that contains textual description, multi-view images, points, and command sequence for each CAD model.
  arXiv  Detail & Related papers  (2024-11-07T18:31:08Z)
• 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.