BRepGround

Text-based B-Rep primitive grounding data for Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding (ICML 2026).

Paper · Code · FutureCAD histories

Data

Each example consists of a STEP solid, a text query, and binary labels on its faces and edges. One Parquet row stores one CAD state; the queries in that row share the same labels. Each label corresponds directly to one STEP primitive.

Split	CAD states	Text queries
train	51,034	153,159
validation	2,837	8,511
test	2,857	8,575

The STEP is the solid before the operation in its state ID. For example, 00133967_chamfer_6 belongs to parent model 00133967. State IDs are disjoint across splits. Parent models overlap: train/validation share 2,039, train/test 2,032, validation/test 272. splits.json uses train, val, and test; Hugging Face Datasets uses validation for the validation split.

OCC executability and selection semantics

OpenCascade (OCC) has limited robustness for fillet, chamfer, and shell on some geometries. During dataset construction, referenced edges or faces for which OCC could not execute the requested operation were filtered out. Consequently, the released target selection can differ from the original Onshape user action; for example, some originally selected edges may be absent. The released entity labels and text queries refer to the retained, OCC-executable target selection.

Load

pip install datasets

from datasets import load_dataset

dataset = load_dataset("jhlee11/BRepGround")
row = dataset["test"][0]
query = row["queries"][0]
face_labels = row["face_labels"]
edge_labels = row["edge_labels"]

Use streaming=True to iterate without downloading the entire split first.

Column	Meaning
id	CAD state ID
queries	Text queries sharing the row's labels
face_labels	One binary label per STEP face, in OCC traversal order
edge_labels	One binary label per STEP edge, in OCC traversal order
asset_archive	ZIP shard containing the STEP and label JSON
step_path, label_path	File paths inside the ZIP shard

1 marks a target primitive; 0 marks an unselected primitive. Face and edge indices each start at zero. Repeated oriented occurrences of the same primitive are counted once, using OCC's IsSame identity comparison.

Download STEP and label files

Download the export script and restore the files:

hf download jhlee11/BRepGround export_data.py --repo-type dataset --local-dir downloads/brepground
python downloads/brepground/export_data.py --split all --output datasets/grounding

Use --split test for one split, --id <state-id> for one state, or --source /path/to/BRepGround for a local repository. Each ZIP shard contains up to 2,000 states. Loading Parquet alone reads the labels and paths; the export script also downloads the STEP files.

datasets/grounding/
  steps/<state-id>.step
  labels/<state-id>.json

Each label JSON contains query, face_labels, and edge_labels; query contains the same strings as the Parquet row's queries.

Read labeled geometry

Label indexing uses pythonocc-core 7.8.1.1 / OCCT 7.8.1. Follow the code repository's environment setup to create the shared futurecad environment. Run the following from the code repository root after exporting the STEP and label files there:

conda activate futurecad

from grounding_brep.step_labels import read_step_labels

sample = read_step_labels(
    "datasets/grounding/steps/00133967_chamfer_6.step",
    "datasets/grounding/labels/00133967_chamfer_6.json",
)
selected_faces = [f for f, flag in zip(sample["faces"], sample["labels"]["face_labels"]) if flag]
selected_edges = [e for e, flag in zip(sample["edges"], sample["labels"]["edge_labels"]) if flag]

The reader loads the STEP with read_step_file(..., as_compound=True) and uses TopologyExplorer(shape, ignore_orientation=True).faces() and .edges(). Labels follow these two traversal sequences directly. Read labels before normalizing, re-exporting, or otherwise modifying the shape. See LABELS.md for the complete indexing convention.

Files

data/<split>-*.parquet
assets/<split>-*.zip
splits.json
export_data.py
read_labels.py
requirements.txt
LABELS.md
README.md
LICENSE.md

License

CC BY 4.0.

Citation

@misc{li2026highfidelitycadgenerationllmdriven,
  title={Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding},
  author={Jiahao Li and Qingwang Zhang and Qiuyu Chen and Guozhan Qiu and Yunzhong Lou and Xiangdong Zhou},
  year={2026},
  eprint={2603.11831},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.11831}
}