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3D-ADS

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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).

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# Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
### [Project](https://www.vision.huji.ac.il/3d_ads) | [Paper](https://arxiv.org/abs/2203.05550) 
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/an-empirical-investigation-of-3d-anomaly/3d-anomaly-detection-and-segmentation-on)](https://paperswithcode.com/sota/3d-anomaly-detection-and-segmentation-on?p=an-empirical-investigation-of-3d-anomaly)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/an-empirical-investigation-of-3d-anomaly/depth-anomaly-detection-and-segmentation-on)](https://paperswithcode.com/sota/depth-anomaly-detection-and-segmentation-on?p=an-empirical-investigation-of-3d-anomaly)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/an-empirical-investigation-of-3d-anomaly/rgb-3d-anomaly-detection-and-segmentation-on)](https://paperswithcode.com/sota/rgb-3d-anomaly-detection-and-segmentation-on?p=an-empirical-investigation-of-3d-anomaly)

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

![](https://raw.githubusercontent.com/eliahuhorwitz/3D-ADS/main/imgs/ours_sum.png)
![](https://raw.githubusercontent.com/eliahuhorwitz/3D-ADS/main/imgs/heatmaps.png)

___

> **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.

![](https://raw.githubusercontent.com/eliahuhorwitz/3D-ADS/main/imgs/rgb_v_3d.png)

## Getting Started

### Setup 
1. Clone the repo:
```bash
git clone https://github.com/eliahuhorwitz/3D-ADS.git
cd 3D-ADS
```
2. Create a new environment and install the libraries:
```bash
python3.7 -m venv 3d_ads_venv
source 3d_ads_venv/bin/activate
pip install -r requirements.txt
```
3. Download and extract the dataset
```bash
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  
```bash
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](https://github.com/eliahuhorwitz/3D-ADS#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 
```bash
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
- 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](https://www.oracle.com/research)
 - The SIFT implementation is based on [Kornia](https://github.com/kornia/kornia)
 - The PatchCore logic is based on [https://github.com/rvorias/ind_knn_ad](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](https://www.mvtec.com/company/research/datasets/mvtec-3d-ad)

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