GenCAD: AI Technology for Generating Editable 3D CAD Models from Images

1. Background and Challenges

In industries like automotive manufacturing, architectural design, and medical device development, 3D CAD models serve as the critical bridge between creative concepts and physical production. Traditional CAD workflows face two persistent challenges:

1. High Operational Complexity: Requires specialized expertise to execute parametric commands for modeling
2. Slow Design Iteration: Manual refinement cycles between conceptual sketches and manufacturable models

Existing AI generation technologies primarily focus on unstructured 3D representations like meshes, voxels, or point clouds. These formats lack the engineering precision needed for direct manufacturing. GenCAD addresses this gap through two key innovations:

• Converting CAD design processes into language-like sequential generation
• Pioneering image-conditioned CAD program synthesis

2. Technical Architecture: Four-Stage Generation Framework

GenCAD employs a modular system to transform visual inputs into editable CAD models:

2.1 CAD Command Language Modeling (CSR Module)

Core Concept: Treat CAD operations as a specialized “language”

| CAD Operation | Parameters | Example Values              |
|---------------|------------|-----------------------------|
| Line          | 2D Points  | End coordinates (x,y)      |
| Arc           | 4D Vector  | End point, angle, direction|
| Extrusion     | 10D Vector | Plane orientation + scaling|

Technical Innovation:

• Uses autoregressive Transformer architecture to model sequential dependencies between CAD commands
• Outperforms traditional models in:

  • Command type prediction accuracy: +0.15%
  • Parameter prediction precision: -0.19% error
  • Long sequence reconstruction (>20 commands): Significant improvement

2.2 Cross-Modal Representation Learning (CCIP Module)

Mechanism:

• Image Encoder: ResNet-18 (pre-trained on ImageNet)
• CAD Encoder: Frozen Transformer from CSR module
• Learning Objective: Contrastive loss to align image and CAD latent spaces

Performance Metrics:

| Input Type   | Retrieval Accuracy (n=2048) |
|--------------|-----------------------------|
| CAD Images   | 61%                         |
| CAD Sketches | 61%                         |

2.3 Conditional Diffusion Prior (CDP Module)

Architecture:

• Forward process: Gradually adds Gaussian noise to CAD latents
• Reverse process: Denoising conditioned on image latents
• Innovation: ResNet-MLP hybrid structure instead of traditional U-Net

2.4 CAD Program Decoding

Final stage uses pre-trained CSR decoder to convert generated latents into complete CAD command sequences. Output is compatible with standard geometry kernels (e.g., OpenCASCADE) for B-rep model generation.

3. Experimental Validation

3.1 Key Metrics

3.2 Comparative Results

| Model          | Generation Type | COV↑  | MMD↑  | JSD↑  |
|----------------|-----------------|-------|-------|-------|
| DeepCAD        | Unconditional   | 78.13 | 1.45  | 3.76  |
| SkexGen        | Unconditional   | 78.17 | 1.55  | 4.89  |
| Brepgen        | Unconditional   | 73.10 | 1.05  | 1.22  |
| GenCAD         | Unconditional   | 78.27 | 1.44  | 3.94  |
| GenCAD-Image   | Conditional     | 81.37 | 1.38  | 3.49  |
| GenCAD-Sketch  | Conditional     | 82.59 | 1.33  | 3.53  |

Key Findings:

• Conditional models show 3-9% improvement in diversity (COV)
• Image-conditional variant achieves lowest FID score (3.1), indicating closest alignment with real data distribution

4. Practical Applications

4.1 Reverse Engineering Automation

Workflow:
Input: Product photos/sketches → Output: Editable CAD models
Use Cases:

• Archaeological artifact digitization
• Competitive product analysis
• Rapid prototyping

4.2 Design Intent Understanding

Features Enabled by Contrastive Learning:

• Image-based CAD library retrieval (60%+ accuracy)
• Design style transfer while preserving structural integrity

4.3 CAD Education

Applications:

• Generate parametric feature demonstrations
• Visualize command sequences for training purposes

5. Current Limitations

1. Vocabulary Constraints:

   • Basic operations only (lines/arcs/extrusions)
   • Missing advanced features (fillets/revolutions/mirroring)

2. Validation Gap:

   • ~3.3% generated commands contain geometric conflicts
   • Requires CAD kernel post-processing

3. Input Requirements:

   • Relies on orthographic projection images
   • Sensitive to complex lighting/backgrounds

6. Future Development Directions

6.1 Functional Expansion

• Add rotational/pattern features
• Support assembly generation

6.2 Verification Feedback

• Integrate CAD kernel for real-time validity checks
• Develop self-correcting generation framework

6.3 Multi-Modal Input

• Support text-image hybrid conditioning
• Develop sketch refinement modules

7. Frequently Asked Questions

Q1: Can GenCAD-generated models be directly used for production?

A: Requires CAD software validation. Current models produce 87% valid outputs; remaining require manual correction.

Q2: Advantages over traditional parametric design?

A: Reduces command input steps by 70%, but complex features still need manual optimization.

Q3: Supported CAD formats?

A: Outputs standard STEP/IGES formats compatible with SolidWorks, AutoCAD, etc.

Q4: Image input requirements?

A: Orthographic projection images recommended. 448x448px grayscale works best.

Q5: Is the system open-source?

A: Code not publicly released, but model weights available through application.

8. Technological Evolution Trends

Current CAD generation is transitioning from “geometric reconstruction” to “semantic design”:

As a third-generation technology, GenCAD provides crucial infrastructure for realizing the “design-as-production” vision in smart manufacturing.