model-compare

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%
=================================================================