Fusion 360 Gallery Segmentation

• Fusion 360 Gallery Segmentation Dataset is a large-scale CAD dataset featuring over 35,000 bodies and 500,000 faces for detailed operation segmentation.
• The dataset includes comprehensive B-rep annotations, multiple geometry representations, and standardized train/validation/test splits for robust evaluation.
• Benchmark results with methods like BRepNet highlight its effectiveness in advancing reverse engineering and geometric reasoning in CAD analysis.

The Fusion 360 Gallery Segmentation Dataset is a large-scale, publicly available benchmark comprising boundary representation (B-rep) models specifically annotated for face-level operation segmentation. Consisting of over 35,000 bodies sampled from the Autodesk Fusion 360 Online Gallery, this dataset provides high-fidelity CAD data structured for robust evaluation of machine learning and geometric reasoning techniques targeting Computer-Aided Design (CAD) model understanding. Each B-rep model is comprehensively labeled with the modeling operation that generated each face, providing a granular testbed for segmentation networks such as BRepNet and for advancing the reverse engineering of CAD workflows (Lambourne et al., 2021).

1. Data Scope, Structure, and Representations

The dataset contains 35,858 B-rep bodies, totaling approximately 500,000 faces (with individual models ranging from 1 to 421 faces and a median of 9). Each B-rep model is described at multiple levels:

• B-rep (Boundary Representation): Provided in Autodesk Fusion 360 (.f3d) and/or STEP format, encoding full parametric geometry (faces, edges, coedges) with explicit sparse adjacency and incidence matrices (N, P, M, F, E).
• Triangle Mesh: High-quality manifold meshes encoded in .obj files with ~3,000 triangles per body.
• Point Cloud: Uniformly sampled sets of 2,048 surface points per body (.ply or .npz).

In addition to raw geometry, Python-readable files (HDF5, pickle) are provided with matrices of per-face, per-edge, and per-coedge features (X^{f} \in \mathbb{R}^{F \times p}, X^{e} \in \mathbb{R}^{E \times q}, X^{c} \in \mathbb{R}^{C \times r}), adjacency matrices, and per-face label vectors $y \in \{1, \ldots, 8\}^F$. Meshes and point clouds inherit face-level labels through index mapping.

Each B-rep’s topology is structured as follows:

Entity	Symbol	Description
Face	$F$	Distinct B-rep patch
Edge	$E$	Curve boundary shared by faces
Coedge	$C$	Directed edge (face–edge pair)

Topology is encoded with five sparse matrices: ‘next’, ‘previous’, and ‘mate’ relations among coedges ($N, P, M \in \{0,1\}^{C \times C}$), and coedge-to-face/edge mappings ($F \in \{0,1\}^{C \times F}$, $E \in \{0,1\}^{C \times E}$).

2. Label Taxonomy and Annotation Schema

The fusion segmentation scheme designates eight exhaustive, mutually exclusive face-level classes reflecting high-level CAD modeling operations:

• ExtrudeSide (side walls of additive extrusion)
• ExtrudeEnd (cap of additive extrusion)
• CutSide (side walls of subtractive extrusion)
• CutEnd (cap of subtractive extrusion)
• RevolveSide (swept surface of non-360° revolve)
• RevolveEnd (end cap of non-360° revolve)
• Fillet
• Chamfer

Each face is labeled with exactly one of these classes. Additionally, for reverse engineering, operation IDs per face enable grouping faces originating from the same modeling action. Extrusions and revolves may be flagged as {Start,Side,End} regarding their generative stages (with RevolveEnd only for sweeps < $360^\circ$). All annotation data is stored in Python/HDF5 files with direct array mapping for ML integration. Mesh and point cloud representations inherit per-face labels via lookup tables between face, triangle, and point indices.

3. Train/Validation/Test Splitting Methodology

The dataset adheres to a 70%/15%/15% split for training, validation, and testing:

Split	Bodies (rounded)
Training	25,080
Validation	5,379
Test	5,399

Splitting is performed post-deduplication to avoid near-duplicate leakage (identical $F$, $E$ plus $F$0 difference in surface area and volume) and all splits disallow any duplicate or near-duplicate overlap.

4. Data Statistics

Label distribution:

Class	Frequency
ExtrudeSide	50.6%
ExtrudeEnd	15.7%
CutSide	11.9%
CutEnd	2.35%
Fillet	10.2%
Chamfer	2.94%
RevolveSide	7.1%
RevolveEnd	0.08%

Model complexity: Median faces per body = 9; 25% have $F$1 faces; maximum is 421. Most bodies (50%) have at least one operation, 31% have at least two, only 1% have more than ten operations (maximum observed: 59).

Sample visualizations in the source work illustrate per-face class mosaics and distribution histograms (Lambourne et al., 2021).

5. Segmentation Task Definition and Evaluation Protocol

The formal segmentation task is: given a B-rep body with $F$2 faces, predict the correct operation-based class $F$3 for each face $F$4.

Evaluation metrics are:

• Per-class intersection-over-union: $F$5
• Mean IoU: $F$6
• Accuracy: $F$7
• Precision, recall, F1: Computed per class $F$8 as: $F$9, $E$0, $E$1

All deep-learning baselines are trained to minimize per-face cross-entropy:

$E$2

Benchmark results: (mean ± std over 10 runs)

Method	Params	Acc (%)	mIoU (%)	Other metrics
BRepNet (winged)	359,100	92.52 ± 0.15	77.10 ± 0.54	—
ECC graph net	359,558	90.36 ± 0.23	72.08 ± 0.50	—
ASM (rule-based)	—	64.57	49.53	—
PointNet++	1,403,784	74.00 ± 0.84	34.78 ± 2.23	Per-point Acc 82.78; IoU 36.49
MeshCNN	2,279,720	62.99 ± 0.37	20.59 ± 0.41	Per-edge Acc 73.81; IoU 24.07

This quantifies the advantage of working on B-rep structures directly, as BRepNet surpasses both mesh- and point-based deep learning methods (Lambourne et al., 2021).

6. Access, Licensing, and Preprocessing

The dataset is released at https://github.com/AutodeskAILab/Fusion360GalleryDataset under the Autodesk Open Source License / CC-BY 4.0 (as per repository). For setup, users are instructed to:

• Clone or download the repository; ensure Python 3 and standard ML libraries are available.
• Load B-rep data using provided scripts to access all feature arrays, matrices, and label vectors.
• For non-B-rep (mesh, point cloud) research, load .obj or .ply files and use bundled mappings from triangle/point indices to B-rep faces.
• All geometries are pre-normalized so that the longest bounding box dimension is 2 units and centered at the origin; no manual scaling or centering is required.

7. Research Applications and Significance

The Fusion 360 Gallery Segmentation Dataset is a foundational resource for learning about CAD structure via B-reps, enabling the development and evaluation of neural architectures (e.g., BRepNet) that leverage topological message passing in solid modeling. Its comprehensive annotation of generative operations facilitates both semantic segmentation and reverse engineering of CAD models. The benchmark has established new standards for accuracy and robustness in operation-based segmentation tasks, providing a reproducible foundation for ongoing research in geometric deep learning and CAD model analysis (Lambourne et al., 2021).