holmes-gu/3d-ads

Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

An Empirical Investigation of 3D Anomaly Detection and Segmentation

Project | Paper

Official PyTorch Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

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An Empirical Investigation of 3D Anomaly Detection and Segmentation
Eliahu Horwitz, Yedid Hoshen
https://arxiv.org/abs/2203.05550

Abstract: Anomaly detection and segmentation (AD&S) in images has made tremendous progress in recent years while 3D information has often been ignored. The objective of this paper is to further understand the benefit and role of 3D as apposed to color in image anomaly detection. Our study begins by presenting a surprising finding: standard color-only anomaly segmentation methods, when applied to 3D datasets, significantly outperform all current methods. On the other hand, we observe that color-only methods are insufficient for images containing geometric anomalies where shape cannot be unambiguously inferred from 2D. This suggests that better 3D methods are needed. We investigate different representations for 3D anomaly detection and discover that hand-crafted orientation-invariant representations are unreasonably effective on this task. We uncover a simple 3D-only method that outperforms all recent approaches while not using deep learning, external pretraining datasets or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with 2D color features, granting us the best current results by a large margin (pixel ROCAUC: 99.2%, PRO: 95.9% on MVTec 3D-AD). We conclude by discussing future challenges for 3D anomaly detection and segmentation.

Getting Started

Setup

1. Clone the repo:

git clone https://github.com/eliahuhorwitz/3D-ADS.git
cd 3D-ADS

2. Create a new environment and install the libraries:

python3.7 -m venv 3d_ads_venv
source 3d_ads_venv/bin/activate
pip install -r requirements.txt

3. Download and extract the dataset

mkdir datasets && cd datasets
mkdir mvtec3d && cd mvtec3d
wget https://www.mydrive.ch/shares/45920/dd1eb345346df066c63b5c95676b961b/download/428824485-1643285832/mvtec_3d_anomaly_detection.tar.xz
tar -xvf mvtec_3d_anomaly_detection.tar.xz

Training

We provide the implementations for 7 methods investigated in the paper. These are:

• RGB iNet
• Depth iNet
• Raw
• HoG
• SIFT
• FPFH
• RGB + FPFH

To run all methods on all 10 classes and save the PRO, Image ROCAUC, Pixel ROCAUC results to markdown tables run

python3 main.py

To change which classes are used, see mvtec3d_classes located at data/mvtec3d.py.
To change which methods are used, see the PatchCore constructor located at patchcore_runner.py and the METHOD_NAMES variable located at main.py.

Note: The results below are of the raw dataset, see the preprocessing section for the preprocessing code and results (as seen in the paper). Note: The pixel-wise metrics benefit from preprocessing. As such, the unprocessed results are slightly below the ones reported in the paper.

AU PRO Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.898	0.948	0.927	0.872	0.927	0.555	0.902	0.931	0.903	0.899	0.876
Depth iNet	0.701	0.544	0.791	0.835	0.531	0.100	0.800	0.549	0.827	0.185	0.586
Raw	0.040	0.047	0.433	0.080	0.283	0.099	0.035	0.168	0.631	0.093	0.191
HoG	0.518	0.609	0.857	0.342	0.667	0.340	0.476	0.893	0.700	0.739	0.614
SIFT	0.894	0.722	0.963	0.871	0.926	0.613	0.870	0.973	0.958	0.873	0.866
FPFH	0.972	0.849	0.981	0.939	0.963	0.693	0.975	0.981	0.980	0.949	0.928
RGB + FPFH	0.976	0.967	0.979	0.974	0.971	0.884	0.976	0.981	0.959	0.971	0.964

Image ROCAUC Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.854	0.840	0.824	0.687	0.974	0.716	0.713	0.593	0.920	0.724	0.785
Depth iNet	0.624	0.683	0.676	0.838	0.608	0.558	0.567	0.496	0.699	0.619	0.637
Raw	0.578	0.732	0.444	0.798	0.579	0.537	0.347	0.306	0.439	0.517	0.528
HoG	0.560	0.615	0.676	0.491	0.598	0.489	0.542	0.553	0.655	0.423	0.560
SIFT	0.696	0.553	0.824	0.696	0.795	0.773	0.573	0.746	0.936	0.553	0.714
FPFH	0.820	0.533	0.877	0.769	0.718	0.574	0.774	0.895	0.990	0.582	0.753
RGB + FPFH	0.938	0.765	0.972	0.888	0.960	0.664	0.904	0.929	0.982	0.726	0.873

Pixel ROCAUC Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.983	0.984	0.980	0.974	0.985	0.836	0.976	0.982	0.989	0.975	0.966
Depth iNet	0.941	0.759	0.933	0.946	0.829	0.518	0.939	0.743	0.974	0.632	0.821
Raw	0.404	0.306	0.772	0.457	0.641	0.478	0.354	0.602	0.905	0.558	0.548
HoG	0.782	0.846	0.965	0.684	0.848	0.741	0.779	0.973	0.926	0.903	0.845
SIFT	0.974	0.862	0.993	0.952	0.980	0.862	0.955	0.996	0.993	0.971	0.954
FPFH	0.995	0.955	0.998	0.971	0.993	0.911	0.995	0.999	0.998	0.988	0.980
RGB + FPFH	0.996	0.991	0.997	0.995	0.995	0.972	0.996	0.998	0.995	0.994	0.993

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Preprocessing

As mentioned in the paper, the results reported use the preprocessed dataset.
While this preprocessing helps in cases where depth images are used, when using the point cloud the results are less pronounced.
It may take a few hours to run the preprocessing. Results for the preprocessed dataset are reported below.

To run the preprocessing

python3 utils/preprocessing.py datasets/mvtec3d/

Note: the preprocessing is performed inplace (i.e. overriding the original dataset)

Preprocessed AU PRO Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.902	0.948	0.929	0.873	0.891	0.570	0.903	0.933	0.909	0.905	0.876
Depth iNet	0.763	0.676	0.884	0.883	0.864	0.322	0.881	0.840	0.844	0.634	0.759
Raw	0.402	0.314	0.639	0.498	0.251	0.259	0.527	0.531	0.808	0.215	0.444
HoG	0.712	0.761	0.932	0.487	0.833	0.520	0.743	0.949	0.916	0.858	0.771
SIFT	0.944	0.845	0.975	0.894	0.909	0.733	0.946	0.981	0.953	0.928	0.911
FPFH	0.974	0.878	0.982	0.908	0.892	0.730	0.977	0.982	0.956	0.962	0.924
RGB + FPFH	0.976	0.968	0.979	0.972	0.932	0.884	0.975	0.981	0.950	0.972	0.959

Preprocessed Image ROCAUC Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.875	0.880	0.777	0.705	0.938	0.720	0.718	0.615	0.859	0.681	0.777
Depth iNet	0.690	0.597	0.753	0.862	0.881	0.590	0.597	0.598	0.791	0.577	0.694
Raw	0.627	0.507	0.600	0.654	0.573	0.524	0.532	0.612	0.412	0.678	0.572
HoG	0.487	0.587	0.691	0.545	0.643	0.596	0.516	0.584	0.507	0.430	0.559
SIFT	0.722	0.640	0.892	0.762	0.829	0.678	0.623	0.754	0.767	0.603	0.727
FPFH	0.825	0.534	0.952	0.783	0.883	0.581	0.758	0.889	0.929	0.656	0.779
RGB + FPFH	0.923	0.770	0.967	0.905	0.928	0.657	0.913	0.915	0.921	0.881	0.878

Preprocessed Pixel ROCAUC Results

Method	Bagel	Cable Gland	Carrot	Cookie	Dowel	Foam	Peach	Potato	Rope	Tire	Mean
RGB iNet	0.983	0.984	0.98	0.974	0.973	0.851	0.976	0.983	0.987	0.977	0.967
Depth iNet	0.957	0.901	0.966	0.970	0.967	0.771	0.971	0.949	0.977	0.891	0.932
Raw	0.803	0.750	0.849	0.801	0.610	0.696	0.830	0.772	0.951	0.670	0.773
HoG	0.911	0.933	0.985	0.823	0.936	0.862	0.923	0.987	0.980	0.955	0.930
SIFT	0.986	0.957	0.996	0.952	0.967	0.921	0.986	0.998	0.994	0.983	0.974
FPFH	0.995	0.965	0.999	0.947	0.966	0.928	0.996	0.999	0.996	0.991	0.978
RGB + FPFH	0.996	0.992	0.997	0.994	0.981	0.973	0.996	0.998	0.994	0.995	0.992

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Citation

If you find this repository useful for your research, please use the following.

@misc{2203.05550,
Author = {Eliahu Horwitz and Yedid Hoshen},
Title = {An Empirical Investigation of 3D Anomaly Detection and Segmentation},
Year = {2022},
Eprint = {arXiv:2203.05550},

Acknowledgments

• This work was supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program. https://www.oracle.com/research
• The SIFT implementation is based on Kornia
• The PatchCore logic is based on https://github.com/rvorias/ind_knn_ad
• The AUPRO implementation is based on the official MVTEC 3D-AD evaluation code found here MVTEC 3D-AD