/3D-ADS

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper (VAND Workshop - CVPR 2023).

Primary LanguagePythonMIT LicenseMIT

Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Project | Paper

PWC

PWC

PWC

Official PyTorch Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.

Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Eliahu Horwitz, Yedid Hoshen
https://arxiv.org/abs/2203.05550

Abstract: Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.

Getting Started

Setup

  1. Clone the repo:
git clone https://github.com/eliahuhorwitz/3D-ADS.git
cd 3D-ADS
  1. 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
  1. 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
  • BTF (Ours)

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
BTF (Ours) 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
BTF (Ours) 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
BTF (Ours) 0.996 0.991 0.997 0.995 0.995 0.972 0.996 0.998 0.995 0.994 0.993


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
BTF (Ours) 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
BTF (Ours) 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
BTF (Ours) 0.996 0.992 0.997 0.994 0.981 0.973 0.996 0.998 0.994 0.995 0.992


Citation

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

@article{horwitz2022empirical,
  title={An Empirical Investigation of 3D Anomaly Detection and Segmentation},
  author={Horwitz, Eliahu and Hoshen, Yedid},
  journal={arXiv preprint arXiv:2203.05550},
  year={2022}
}

Acknowledgments