metadata
license: mit
library_name: pytorch
tags:
- 3d-anomaly-detection
- point-cloud
- real3d-ad
- anomalyshapenet
- af3ad
AF3AD Checkpoints
Pretrained AF3AD PO3AD-style checkpoints for Real3D-AD and AnomalyShapeNet.
Each checkpoint is packaged with a small YAML config per category.
The current AnomalyShapeNet release includes 37 categories; the remaining categories will be added later.
Usage
Clone or download this repository into the project as ckpts/:
ckpts/Real3DAD/[category]/ckpts/[category].pth
ckpts/Real3DAD/[category]/po3ad_eval_real3d.yaml
ckpts/AnomalyShapeNet/[category]/ckpts/[category].pth
ckpts/AnomalyShapeNet/[category]/po3ad_eval_anomalyshapenet.yaml
From the AF3AD repo root:
export PYTHONPATH="$PWD:$PYTHONPATH"
python3 scripts/evaluate_po3ad_checkpoint.py --checkpoint ckpts/Real3DAD/airplane/ckpts/airplane.pth --config ckpts/Real3DAD/airplane/po3ad_eval_real3d.yaml
python3 scripts/evaluate_po3ad_checkpoint.py --checkpoint ckpts/AnomalyShapeNet/ashtray0/ckpts/ashtray0.pth --config ckpts/AnomalyShapeNet/ashtray0/po3ad_eval_anomalyshapenet.yaml
Replace airplane or ashtray0 with any available category from the matching dataset.
To evaluate all currently packaged AnomalyShapeNet categories:
bash ckpts/AnomalyShapeNet/eval_commands.sh
Citation
@misc{balapour2026anomalyfactory3dmodular,
title={Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection},
author={Ali Balapour and Faraz Hach},
year={2026},
eprint={2606.29181},
archivePrefix={arXiv},
primaryClass={cs.CV}
}