name: model-compare
description: Compare 3D CAD models using boolean operations (IoU, Dice, precision/recall). Use when evaluating generated models against gold references, diffing CAD revisions, or computing similarity metrics for ML training. Triggers on: model diff, compare models, IoU, intersection over union, model similarity, CAD comparison, STEP diff, 3D evaluation, gold reference, generated model, precision recall 3D.

3D Model Comparison Tool

Compare CAD models using boolean operations to compute similarity metrics like IoU, Dice, precision, and recall. Useful for:

• Evaluating ML-generated models against gold references
• Comparing revisions of CAD designs
• Computing metrics for training 3D generative models
• Visualizing geometric differences

Quick Start

# Compare two STEP files
uvx --from build123d python scripts/model_diff.py reference.step generated.step

# JSON output for training pipelines
uvx --from build123d python scripts/model_diff.py ref.step gen.step --json --no-export

# Demo mode (no files needed)
uvx --from build123d python scripts/model_diff.py --demo

Supported Formats

Output Metrics

Primary Metrics (for ML training)

Diagnostic Metrics

Interpretation

The tool provides automatic interpretation:
- Over-generating: Low precision, high extra geometry
- Under-generating: Low recall, missing geometry
- Size issues: Volume ratio far from 1.0
- Position issues: Large center offset

CLI Options

usage: model_diff.py [-h] [-o OUTPUT_DIR] [--json] [--no-export] [--demo]
                     [reference] [generated]

positional arguments:
  reference          Reference/gold model file (STEP, BREP, or STL)
  generated          Generated/predicted model file to compare

options:
  -o, --output-dir   Output directory for GLB files (default: .)
  --json             Output only JSON metrics (for pipelines)
  --no-export        Skip exporting GLB visualization files
  --demo             Run with built-in demo models

Output Files

When --no-export is not set, produces GLB files for visualization:

Example: Training Pipeline Integration

# Batch evaluation
for gen in outputs/*.step; do
    uvx --from build123d python model_diff.py gold.step "$gen" --json --no-export
done | jq -s '{
    avg_iou: (map(.iou) | add / length),
    avg_precision: (map(.precision) | add / length),
    avg_recall: (map(.recall) | add / length)
}'

Example: Loss Function

# In your training code, use metrics for loss:
loss = (
    (1 - metrics['iou']) * 1.0 +           # Primary shape match
    abs(1 - metrics['volume_ratio']) * 0.5 + # Scale accuracy
    metrics['center_offset'] * 0.1           # Position accuracy
)

How It Works

The tool uses boolean operations from OpenCASCADE (via build123d):

Missing  = Reference - Generated  (A - B)
Extra    = Generated - Reference  (B - A)
Common   = Reference & Generated  (A ∩ B)
Union    = Reference + Generated  (A ∪ B)

IoU      = volume(Common) / volume(Union)
Dice     = 2 * volume(Common) / (volume(A) + volume(B))
Precision = volume(Common) / volume(B)
Recall    = volume(Common) / volume(A)

Sample Output

=================================================================
  3D MODEL COMPARISON REPORT
  Reference (A) vs Generated (B)
=================================================================

──────────────────────────── VOLUMES ────────────────────────────
  Reference (A):          51,433.629
  Generated (B):          45,904.426
  Intersection (A∩B):     42,292.031
  Missing (A-B):           9,141.598  (17.8% of A)
  Extra (B-A):             3,612.395  (7.9% of B)

──────────────────────── PRIMARY METRICS ────────────────────────
  IoU (Jaccard):              0.7683  (1.0 = identical)
  Dice (F1):                  0.8690  (1.0 = identical)
  Precision:                  0.9213  (correctness of B)
  Recall:                     0.8223  (coverage of A)

──────────────────────── INTERPRETATION ─────────────────────────
  △ Partial match (IoU > 50%)
  → Under-generating: 17.8% of A is missing
  → Undersized by 10.8%
=================================================================