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annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- feature-extraction
- image-feature-extraction
- text-to-3d
- image-to-3d
- other
pretty_name: PNI-3D Multimodal CAD & Geometric Robustness Benchmark
dataset_info:
features:
- name: family_id
dtype: string
- name: family_type
dtype: string
- name: nominal_parameters
dtype: string
- name: variant_parameters
dtype: string
- name: nuisance_parameters
dtype: string
- name: same_part_group
sequence: string
- name: functional_variant_group
sequence: string
- name: near_duplicates
sequence: string
- name: negative_pairs
sequence: string
tags:
- 3d-cad
- geometric-ai
- point-cloud
- step
- mesh
- cross-modal-search
thumbnail: pni3d_overview.png
---
# PNI-3D: Multimodal CAD & Geometric AI Benchmark
**PNI-3D** is a benchmark dataset designed for testing fine-grained 3D CAD matching, multimodal embedding alignment, and geometric retrieval under real-world sensor distortions.
---
## **Key Dataset Metrics**
* **100 Parametric CAD Families:** Mechanical engineering primitives (flanges, mounting brackets, U-channels, spur gears, shafts, heat sinks, valve housings, and more).
* **2,400 Total Assets:** Pairs of nominal parts and functional variants, each in 12 representations.
* **12 Multimodal Representations per Part:** Native parametric CAD, polygon meshes, point clouds, voxel grids, and 2D depth projections.
* **Explicit Ground Truth:** same-part groups, functional-variant groups, near-duplicate sets, negative pairs, and quantitative nuisance parameters.
---
## **12 Representations Per Part**
Each parametric CAD instance is represented across 12 distinct formats to benchmark cross-modal embedding models:
| Category | Representation | Description |
| :--- | :--- | :--- |
| **Native CAD** | `_STEP` | Boundary Representation (B-Rep) STEP file. |
| | `_OBJ` | Polygon mesh export. |
| **Meshes** | `_MESH_FINE` | High-density watertight triangulation. |
| | `_MESH_COARSE` | Decimated lightweight geometry (25% face count). |
| | `_MESH_CORRUPTED` | Non-manifold mesh with missing facets and flipped normals. |
| **Point Clouds** | `_PCD_CLEAN` | Dense uniform surface point sample (N = 10,000). |
| | `_PCD_NOISY` | Point cloud with Gaussian displacement along surface normals (σ = 0.5 mm). |
| | `_PCD_SPARSE` | Decimated low-density point cloud (N = 500). |
| **Sensor / Spatial** | `_DEPTH_MAP` | Multi-view 128 × 128 normalized depth array (`.npy`). |
| | `_VOXEL` | 32 × 32 × 32 spatial occupancy grid (`.npy`). |
| **Transformations** | `_NUISANCE_ROTATED` | Arbitrary SO(3) rotation (Euler angles recorded per family). |
| | `_NUISANCE_SCALED` | Anisotropic scaling factors in [0.80×, 1.25×] (recorded per family). |
---
## **Part Varieties & Parameter Splits**
PNI-3D isolates **nominal CAD geometry** from **functional variants** to evaluate whether geometric models recognize subtle topological changes (like bolt-hole counts or thickness adjustments):
```json
{
"family_id": "FAM_001",
"family_type": "flange",
"nominal_parameters": {
"outer_diameter": 83.74,
"bore_diameter": 25.14,
"thickness": 13.19,
"bolt_holes": 8,
"bolt_circle_radius": 32.33,
"fillet_radius": 2.82
},
"variant_parameters": {
"outer_diameter": 83.74,
"bore_diameter": 25.14,
"thickness": 18.19,
"bolt_holes": 10,
"bolt_circle_radius": 32.33,
"fillet_radius": 0.0
},
"nuisance_parameters": {
"rotation_angles_deg": [127.3, 34.677, -159.243],
"scale_factors": [1.0313, 1.1479, 1.1917],
"noise_sigma_mm": 0.5,
"decimation_ratio": 0.25,
"voxel_resolution": 32
},
"same_part_group": [
"P_001_ORIGINAL_STEP",
"P_001_ORIGINAL_MESH_FINE",
"P_001_ORIGINAL_PCD_CLEAN",
"P_001_ORIGINAL_VOXEL"
],
"near_duplicates": [
"P_001_ORIGINAL_MESH_FINE",
"P_001_ORIGINAL_MESH_COARSE",
"P_001_ORIGINAL_OBJ"
]
}
```
The 12 CAD categories are: flange, spur gear, mounting bracket, heat sink, valve housing,
pipe fitting, hex bolt, stepped shaft, pulley, gusset, collar, and U-channel.
## **Repository Contents**
| Path | Description |
| :--- | :--- |
| `metadata.parquet` | 100-row viewer table (schema above). |
| `ground_truth_index.json` | Full ground-truth index (all groups + nuisance parameters). |
| `manifest.csv` | Asset-level index (family, role, representation, path). |
| `assets/FAM_*/` | All 2,400 asset files (12 per part × 2 parts × 100 families). |
| `generate_pni3d.py` | Parametric generator (reproduce / scale to 600k). |
| `evaluate_pni3d.py` | Retrieval + Samples-to-Threshold benchmark harness. |
| `pni3d_overview.png` | Pipeline overview. |
## **Quick Start Usage**
```python
from datasets import load_dataset
dataset = load_dataset("tryforge/pni-3d-benchmark")
# Inspect a sample part family
sample = dataset["train"][0]
print("Family Type:", sample["family_type"])
print("Nominal Bolt Holes:", sample["nominal_parameters"]["bolt_holes"])
print("Variant Bolt Holes:", sample["variant_parameters"]["bolt_holes"])
```
## **Contact & Support**
If you encounter any issues accessing the dataset or require additional parameter sweeps, reach out directly:
Email: ravi@getforge.tech
LinkedIn: https://www.linkedin.com/in/raveekumar1/
|