metadata
license: apache-2.0
language:
- en
base_model: google-bert/bert-large-uncased
datasets:
- jhlee11/BRepGround
tags:
- cad
- brep
- grounding
- pytorch
BRepGround
BRepGround grounds a text query to faces and edges of a CAD B-Rep.
Usage
Install the futurecad environment from the code repository.
Run from that repository's root:
hf download jhlee11/BRepGround --local-dir weights/brepground
python -m grounding_brep.serve \
--config weights/brepground/config.yaml \
--checkpoint weights/brepground/model.safetensors
To evaluate the model, prepare the
STEP dataset's graph cache and run bash scripts/evaluate_grounding.sh.
See dataset usage
for the label-reading convention and commands.
The model uses BERT-large-uncased
as its text encoder. Its tokenizer and configuration are included in
bert-large-uncased/.
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},
}