rawwerks/vibecad/model-compare

Name: model-compare
Author: rawwerks

Claude Code and agent skill by rawwerks/vibecad

Trust tier 2

community

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By rawwerks
AI/ML
rawwerks/vibecad
Plugin build123d
plugins/build123d/skills/model-compare/SKILL.md
Updated 26 Oct 2026
Version 1.0.01 version recorded

Source check pending

Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install rawwerks/vibecad/model-compare

SKILL.md

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

bash

# 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

Format	Extension	Notes
STEP	`.step`, `.stp`	Recommended - full CAD fidelity
BREP	`.brep`	OpenCASCADE native format
STL	`.stl`	Mesh format - may have boolean issues

Output Metrics

Primary Metrics (for ML training)

Metric	Range	Description
IoU (Jaccard)	0-1	`	A∩B	/	A∪B	` - standard similarity
Dice (F1)	0-1	`2	A∩B	/ (	A	+	B	)` - more sensitive to small overlaps
Precision	0-1	`	A∩B	/	B	` - how much of generated is correct
Recall	0-1	`	A∩B	/	A	` - how much of reference was captured

Diagnostic Metrics

Metric	Description
`volume_ratio`	B/A volume ratio (1.0 = same size)
`center_offset`	Distance between centers of mass
`bbox_iou`	Bounding box IoU (coarse alignment)
`size_ratio_x/y/z`	Per-axis scale comparison
`surface_ratio`	Surface area comparison

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:

File	Description
`diff_reference.glb`	The reference model (A)
`diff_generated.glb`	The generated model (B)
`diff_missing.glb`	Geometry in A but not B (under-generation)
`diff_extra.glb`	Geometry in B but not A (over-generation)
`diff_common.glb`	Geometry in both (correct match)

Example: Training Pipeline Integration

bash

# 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

python

# 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%
=================================================================

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Source

SKILL.md in the repositoryCheckingplugins/build123d/skills/model-compare/SKILL.md

Popularity

Repository stars: 123
Repository forks: 17
7-day trend score: 0

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