Detecting Anomalous 3D Point Clouds Using Pre-Trained Feature Extractors

Dario Mantegazza, Alessandro Giusti

2024

Abstract

In this paper we explore the status of the research effort for the task of 3D visual anomaly detection; in particular, we investigate whether it is possible to find anomalies on 3D point clouds using off-the-shelf feature extractors, similar to what is already feasible on images, without the requirement of an ad-hoc one. Our work uses a model composed of two parts: a feature extraction module and an anomaly detection head. The latter is fixed and works on the embeddings from the feature extraction module. Using the MVTec-3D dataset, we contribute a comparison between a 3D point cloud features extractor, a 2D image features extractor, a combination of the two, and three baselines. We also compare our work with other models on the dataset’s DETECTION-AUROC benchmark. The experiment results demonstrate that, while our proposed approach surpasses the baselines and some other approaches, our best-performing model cannot beat purposely developed ones. We conclude that a combination of dataset size and 3D data complexity is the culprit to a lack of off-the-shelf feature extractors for solving complex 3D vision tasks.

Paper Citation

in Harvard Style

Mantegazza D. and Giusti A. (2024). Detecting Anomalous 3D Point Clouds Using Pre-Trained Feature Extractors. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 373-380. DOI: 10.5220/0012465600003660

in Bibtex Style

@conference{visapp24,
author={Dario Mantegazza and Alessandro Giusti},
title={Detecting Anomalous 3D Point Clouds Using Pre-Trained Feature Extractors},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={373-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465600003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Detecting Anomalous 3D Point Clouds Using Pre-Trained Feature Extractors
SN - 978-989-758-679-8
AU - Mantegazza D.
AU - Giusti A.
PY - 2024
SP - 373
EP - 380
DO - 10.5220/0012465600003660
PB - SciTePress