Turning Cabinet Drawings into 3D Models with AI

Construction and cabinet manufacturing still rely heavily on PDF drawings.

Designers create them.
Clients approve them.
But when production begins, someone still needs to manually convert those drawings into CAD models or 3D layouts.

That process is slow.

And repetitive.

So we asked a simple question:

Can AI convert cabinet drawings directly into usable 3D data?

This project explores how we built a system that reads cabinet drawings from PDFs and converts them into structured geometry that can generate DWG and 3D models.

The Real Problem

Cabinet drawings contain a lot of useful information:

• Layout structure
• Cabinet boundaries
• Measurements
• Labels
• Door and drawer positions

But most of this information exists in visual form.

Machines cannot easily interpret that.

Traditional automation tools fail because they expect structured CAD data, not messy PDF drawings.

So the challenge was:

How do we convert visual architectural information into structured geometry?

System Overview

The pipeline we designed combines computer vision, detection models, and language models.

The workflow looks like this:

• PDF is converted into images
• Computer vision detects cabinets and components
• Text extraction captures measurements
• LLM interprets dimensions and structure
• Structured geometry is generated
• DWG / 3D models are produced

Each step solves a specific problem.

Step 1 — Detecting Cabinets with Computer Vision

We trained a YOLO-based object detection model to identify cabinet components inside drawings.

The model detects:

• Base cabinets
• Wall cabinets
• Tall cabinets
• Appliances
• Structural boundaries

Why YOLO?

Because it provides fast detection with high spatial accuracy, which is critical when working with architectural drawings.

Once detected, the system extracts bounding boxes and spatial relationships between cabinets.

This becomes the foundation for geometry reconstruction.

Step 2 — Extracting Measurements

Cabinet drawings include important measurements like:

• Width
• Height
• Depth
• Spacing between cabinets

We use OCR pipelines to extract measurement text from the drawing.

But raw text is messy.

For example:

W 36"
H 34 1/2"
D 24"

This is where AI interpretation becomes necessary.

Step 3 — Using an LLM to Understand Dimensions

The extracted text is passed to an LLM layer that converts ambiguous measurements into structured data.

Example:

Raw text:

36 W x 34.5 H x 24 D

Converted into structured format:

{
 width: 36,
 height: 34.5,
 depth: 24
}

The LLM also resolves:

• inconsistent labels
• missing context
• measurement formatting

This step turns visual annotations into reliable numerical data.

Step 4 — Reconstructing Cabinet Geometry

Once we have:

• cabinet detection
• dimensions
• layout relationships

We can generate structured cabinet geometry.

Each cabinet becomes a parametric object like:

Cabinet {
  type: Base
  width: 36
  height: 34.5
  depth: 24
  position: (x,y)
}

From this structure we can generate:

• 3D models
• AutoCAD DWG files
• manufacturing layouts

Step 5 — Exporting CAD and 3D Models

The final step converts structured geometry into formats used by design tools.

Outputs include:

• DWG files
• 3D cabinet assemblies
• layout visualizations

At this stage, designers can directly open the results inside CAD software.

What previously required hours of manual work can now be generated automatically.

Key Challenges We Faced

Building this system exposed several real-world problems.

Drawing Variability

No two cabinet drawings are identical.

Different designers use different:

• annotation styles
• measurement formats
• symbols

The AI system must handle high variation.

Scaling and Measurement Accuracy

Architectural drawings use scaled representations.

We had to design logic that converts pixel measurements into real-world dimensions.

Even small errors could break cabinet assembly.

Spatial Relationships

Cabinets are not isolated objects.

They depend on:

• walls
• appliances
• adjacent cabinets

The system must understand layout context, not just object detection.

What This Enables

Automating cabinet interpretation unlocks several possibilities:

Faster cabinet design workflows

Automated CAD generation

Reduced manual drafting work

Faster manufacturing preparation

In the future, systems like this could process entire architectural plans, not just cabinets.

Final Thoughts

Most industries still rely on documents created for humans, not machines.

But with the right combination of:

• computer vision
• detection models
• language models

we can convert visual design documents into structured data pipelines.

Cabinet drawings are just one example.

The bigger opportunity lies in automating how machines read and understand design documents.