# CADDreamer: CAD Object Generation from Single-view Images

arXiv: https://arxiv.org/abs/2502.20732 · HTML: https://mlsanity.com/paper/2502.20732/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 34)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://mlsanity.com/paper/2105.09492/) (2021)
- [TripoSR: Fast 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/2403.02151/) (2024)
- [CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model](https://mlsanity.com/paper/2403.05034/) (2024)
- [MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers](https://mlsanity.com/paper/2406.10163/) (2024)
- [Rethinking Inductive Biases for Surface Normal Estimation](https://mlsanity.com/paper/2403.00712/) (2024)
- [BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry](https://mlsanity.com/paper/2401.15563/) (2024)
- [Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention](https://mlsanity.com/paper/2405.11616/) (2024)
- [SolidGen: An Autoregressive Model for Direct B-rep Synthesis](https://mlsanity.com/paper/2203.13944/) (2022)
- [IM2CAD](https://mlsanity.com/paper/1608.05137/) (2016)
- [UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree](https://mlsanity.com/paper/2006.09102/) (2020)
- [ROCA: Robust CAD Model Retrieval and Alignment from a Single Image](https://mlsanity.com/paper/2112.01988/) (2021)
- [Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve](https://mlsanity.com/paper/2007.13034/) (2020)
- [CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly](https://mlsanity.com/paper/2104.05652/) (2021)
- [DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image](https://mlsanity.com/paper/2311.18610/) (2023)
- [Reconstructing editable prismatic CAD from rounded voxel models](https://mlsanity.com/paper/2209.01161/) (2022)
- [GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors](https://mlsanity.com/paper/2409.16294/) (2024)
- [Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometry](https://mlsanity.com/paper/2410.03417/) (2024)

## Built on by (most cited first, 25 of 25)

- [Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek](https://mlsanity.com/paper/2505.17702/) (2025)
- [cadrille: Multi-modal CAD Reconstruction with Reinforcement Learning](https://mlsanity.com/paper/2505.22914/) (2025)
- [ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models](https://mlsanity.com/paper/2512.06328/) (2025)
- [CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation](https://mlsanity.com/paper/2605.10873/) (2026)
- [Towards Geometric and Textural Consistency 3D Scene Generation via Single Image-guided Model Generation and Layout Optimization](https://mlsanity.com/paper/2507.14841/) (2025)
- [CADReasoner: Iterative Program Editing for CAD Reverse Engineering](https://mlsanity.com/paper/2603.29847/) (2026)
- [CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models](https://mlsanity.com/paper/2605.01925/) (2026)
- [CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent](https://mlsanity.com/paper/2508.01031/) (2025)
- [CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization](https://mlsanity.com/paper/2605.01171/) (2026)
- [neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing](https://mlsanity.com/paper/2604.16170/) (2026)
- [CADKnitter: Compositional CAD Generation from Text and Geometry Guidance](https://mlsanity.com/paper/2512.11199/) (2025)
- [Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision](https://mlsanity.com/paper/2606.29301/) (2026)
- [SOV-CAD: Stepwise Orthographic Views Guided CAD Modeling Sequence Reconstruction](https://mlsanity.com/paper/2607.04119/) (2026)
- [CADForge: Agentic Single-View CAD Reconstruction with Explicit Geometry Reasoning](https://mlsanity.com/paper/2610.04262/) (2026)
- [Flatten The Complex: Joint B-Rep Generation via Compositional k-Cell Particles](https://mlsanity.com/paper/2601.17733/) (2026)
- [BrepGaussian: CAD reconstruction from Multi-View Images with Gaussian Splatting](https://mlsanity.com/paper/2602.21105/) (2026)
- [A2Z-10M+: Geometric Deep Learning with A-to-Z BRep Annotations for AI-Assisted CAD Modeling and Reverse Engineering](https://mlsanity.com/paper/2603.12605/) (2026)
- [Arko-T: A Foundation Model for Text-to-Structured 3D Generation](https://mlsanity.com/paper/2606.30429/) (2026)
- [TraceCAD: Trace-Guided Repair for Agentic CAD Generation](https://mlsanity.com/paper/2608.03062/) (2026)
- [Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization](https://mlsanity.com/paper/2608.10602/) (2026)
- [LLM-Aided Design for Manufacturing: A Multi-Agent System for Intent-Preserving Redesign of CAD for Improved Manufacturability](https://mlsanity.com/paper/2609.05559/) (2026)
- [VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding](https://mlsanity.com/paper/2609.21225/) (2026)
- [AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing](https://mlsanity.com/paper/2609.29621/) (2026)
- [StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search](https://mlsanity.com/paper/2610.03799/) (2026)
- [UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation](https://mlsanity.com/paper/2610.04092/) (2026)

## Abstract (arXiv)

Diffusion-based 3D generation has made remarkable progress in recent years. However, existing 3D generative models often produce overly dense and unstructured meshes, which stand in stark contrast to the compact, structured, and sharply-edged Computer-Aided Design (CAD) models crafted by human designers. To address this gap, we introduce CADDreamer, a novel approach for generating boundary representations (B-rep) of CAD objects from a single image. CADDreamer employs a primitive-aware multi-view diffusion model that captures both local geometric details and high-level structural semantics during the generation process. By encoding primitive semantics into the color domain, the method leverages the strong priors of pre-trained diffusion models to align with well-defined primitives. This enables the inference of multi-view normal maps and semantic maps from a single image, facilitating the reconstruction of a mesh with primitive labels. Furthermore, we introduce geometric optimization techniques and topology-preserving extraction methods to mitigate noise and distortion in the generated primitives. These enhancements result in a complete and seamless B-rep of the CAD model. Experimental results demonstrate that our method effectively recovers high-quality CAD objects from single-view images. Compared to existing 3D generation techniques, the B-rep models produced by CADDreamer are compact in representation, clear in structure, sharp in edges, and watertight in topology.