VALERIE22
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# VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/teaser_c.png">
## Dataset Description
- **Paper:** https://arxiv.org/abs/2308.09632
- **Point of Contact:** korbinian.hagn@intel.com
### Dataset Summary
The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline (see image below) providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs.
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/VALERIE_overview1.png">
Each sequence of the dataset contains for each scene two rendered images. One is rendered with the default Blender tonemapping (/png) whereas the second is renderd with our photorealistic sensor simulation (see hagn2022optimized). The image below shows the difference of the two methods.
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/SensorSimulation.png">
Following are some example images showing the unique characteristics of the different sequences.
|Sequence0052|Sequence0054|Sequence0057|Sequence0058|
|:---:|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq52_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq54_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq57_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq58_1.png" width="500">|
|Sequence0059|Sequence0060|Sequence0062|
|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq59_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq60_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq62_1.jpg" width="500">|
### Supported Tasks
- pedestrian detection
- 2d object-detection
- 3d object-detection
- semantic-segmentation
- instance-segmentation
- ai-validation
## Dataset Structure
```
VALERIE22
└───intel_results_sequence_0050
│ └───ground-truth
│ │ └───2d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───3d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───class-id_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───general-globally-per-frame-analysis_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ │ └───car-camera000-0000-{UUID}-0000.csv
│ │ └───semantic-group-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───semantic-instance-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ └───car-camera000-0000-{UUID}-0000
│ │ │ │ └───{Entity-ID}
│ └───sensor
│ │ └───camera
│ │ │ └───left
│ │ │ │ └───png
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ │ └───png_distorted
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
└───intel_results_sequence_0052
└───intel_results_sequence_0054
└───intel_results_sequence_0057
└───intel_results_sequence_0058
└───intel_results_sequence_0059
└───intel_results_sequence_0060
└───intel_results_sequence_0062
```
### Data Splits
13476 images for trainining:
```
dataset = load_dataset("Intel/VALERIE22", split="train")
```
8406 images for validation and test:
```
dataset = load_dataset("Intel/VALERIE22", split="validation")
dataset = load_dataset("Intel/VALERIE22", split="test")
```
### Licensing Information
CC BY 4.0
## Grant Information
Generated within project KI-Abischerung with funding of the German Federal Ministry of Industry and Energy under grant number 19A19005M.
### Citation Information
Relevant publications:
```
@misc{grau2023valerie22,
title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},
author={Oliver Grau and Korbinian Hagn},
year={2023},
eprint={2308.09632},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{hagn2022increasing,
title={Increasing pedestrian detection performance through weighting of detection impairing factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2022}
}
@inproceedings{hagn2022validation,
title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={European Conference on Computer Vision},
pages={476--491},
year={2022},
organization={Springer}
}
@incollection{grau2022variational,
title={A variational deep synthesis approach for perception validation},
author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={359--381},
year={2022},
publisher={Springer International Publishing Cham}
}
@incollection{hagn2022optimized,
title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={127--147},
year={2022},
publisher={Springer International Publishing Cham}
}
@inproceedings{syed2020dnn,
title={DNN analysis through synthetic data variation},
author={Syed Sha, Qutub and Grau, Oliver and Hagn, Korbinian},
booktitle={Proceedings of the 4th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2020}
}
```
# VALERIE22 - 高真实感、富含元数据标注的城市环境数据集
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/teaser_c.png">
## 数据集说明
- **论文链接：** https://arxiv.org/abs/2308.09632
- **联系方式：** korbinian.hagn@intel.com
### 数据集概述
VALERIE22数据集基于VALERIE程序化工具流水线（详见下图）生成，通过自动合成场景渲染得到高真实感的传感器仿真数据。该数据集附带极为丰富的元数据，支持提取特定场景与语义特征（如像素级精确的遮挡率、场景内位置、与相机的距离及角度），可支撑多样化的数据分析实验，我们期望以此推动对深度神经网络（Deep Neural Network, DNN）性能理解的相关研究。
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/VALERIE_overview1.png">
数据集的每个序列针对每个场景包含两张渲染图像：一张采用默认的Blender色调映射（格式为PNG），另一张则采用我们的高真实感传感器仿真渲染（详见hagn2022optimized）。下图展示了两种渲染方法的差异。
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/SensorSimulation.png">
以下为若干示例图像，展示了不同序列的独特特性：
|Sequence0052|Sequence0054|Sequence0057|Sequence0058|
|:---:|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq52_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq54_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq57_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq58_1.png" width="500">|
|Sequence0059|Sequence0060|Sequence0062|
|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq59_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq60_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq62_1.jpg" width="500">|
### 支持任务
- 行人检测
- 二维目标检测
- 三维目标检测
- 语义分割
- 实例分割
- AI验证
### 数据集结构
VALERIE22
└───intel_results_sequence_0050
│ └───ground-truth
│ │ └───2d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───3d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───class-id_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───general-globally-per-frame-analysis_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ │ └───car-camera000-0000-{UUID}-0000.csv
│ │ └───semantic-group-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───semantic-instance-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ └───car-camera000-0000-{UUID}-0000
│ │ │ │ └───{Entity-ID}
│ └───sensor
│ │ └───camera
│ │ │ └───left
│ │ │ │ └───png
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ │ └───png_distorted
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
└───intel_results_sequence_0052
└───intel_results_sequence_0054
└───intel_results_sequence_0057
└───intel_results_sequence_0058
└───intel_results_sequence_0059
└───intel_results_sequence_0060
└───intel_results_sequence_0062
### 数据划分
13476张图像用于训练：
dataset = load_dataset("Intel/VALERIE22", split="train")
8406张图像用于验证与测试：
dataset = load_dataset("Intel/VALERIE22", split="validation")
dataset = load_dataset("Intel/VALERIE22", split="test")
### 授权信息
知识共享署名4.0（CC BY 4.0）
## 资助信息
本数据集在KI-Abischerung项目框架下生成，获得德国联邦工业与能源部资助，资助编号为19A19005M。
### 引用信息
相关出版物：
@misc{grau2023valerie22,
title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},
author={Oliver Grau and Korbinian Hagn},
year={2023},
eprint={2308.09632},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{hagn2022increasing,
title={Increasing pedestrian detection performance through weighting of detection impairing factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2022}
}
@inproceedings{hagn2022validation,
title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={European Conference on Computer Vision},
pages={476--491},
year={2022},
organization={Springer}
}
@incollection{grau2022variational,
title={A variational deep synthesis approach for perception validation},
author={Oliver Grau and Korbinian Hagn and Syed Sha, Qutub},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={359--381},
year={2022},
publisher={Springer International Publishing Cham}
}
@incollection{hagn2022optimized,
title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={127--147},
year={2022},
publisher={Springer International Publishing Cham}
}
@inproceedings{syed2020dnn,
title={DNN analysis through synthetic data variation},
author={Syed Sha, Qutub and Grau, Oliver and Hagn, Korbinian},
booktitle={Proceedings of the 4th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2020}
}
提供机构：
maas
创建时间：
2025-08-01
