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--- |
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license: cc0-1.0 |
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task_categories: |
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- depth-estimation |
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tags: |
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- omnidirectional |
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- stereo |
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- depth |
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- estimation |
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pretty_name: Helvipad |
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size_categories: |
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- 10K<n<100K |
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--- |
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# <span style="font-variant: small-caps;">Helvipad</span>: A Real-World Dataset for Omnidirectional Stereo Depth Estimation |
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[](https://arxiv.org/abs/2411.18335) |
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[](https://vita-epfl.github.io/Helvipad/) |
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## Abstract |
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Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, |
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mainly due to the lack of appropriate data. |
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We introduce <span style="font-variant: small-caps;">Helvipad</span>, |
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a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K frames from video sequences |
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across diverse environments, including crowded indoor and outdoor scenes with diverse lighting conditions. |
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Collected using two 360° cameras in a top-bottom setup and a LiDAR sensor, the dataset includes accurate |
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depth and disparity labels by projecting 3D point clouds onto equirectangular images. Additionally, we |
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provide an augmented training set with a significantly increased label density by using depth completion. |
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We benchmark leading stereo depth estimation models for both standard and omnidirectional images. |
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The results show that while recent stereo methods perform decently, a significant challenge persists in accurately |
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estimating depth in omnidirectional imaging. To address this, we introduce necessary adaptations to stereo models, |
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achieving improved performance. |
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## Dataset Structure |
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The dataset is organized into training and testing subsets with the following structure: |
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``` |
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helvipad/ |
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├── train/ |
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│ ├── depth_maps # Depth maps generated from LiDAR data |
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│ ├── depth_maps_augmented # Augmented depth maps using depth completion |
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│ ├── disparity_maps # Disparity maps computed from depth maps |
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│ ├── disparity_maps_augmented # Augmented disparity maps using depth completion |
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│ ├── images_top # Top-camera RGB images |
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│ ├── images_bottom # Bottom-camera RGB images |
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│ ├── LiDAR_pcd # Original LiDAR point cloud data |
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├── test/ |
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│ ├── depth_maps # Depth maps generated from LiDAR data |
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│ ├── disparity_maps # Disparity maps computed from depth maps |
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│ ├── images_top # Top-camera RGB images |
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│ ├── images_bottom # Bottom-camera RGB images |
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│ ├── LiDAR_pcd # Original LiDAR point cloud data |
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``` |
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## Benchmark |
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We evaluate the performance of multiple state-of-the-art and popular stereo matching methods, both for standard and 360° images. All models are trained on a single NVIDIA A100 GPU with |
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the largest possible batch size to ensure comparable use of computational resources. |
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| Method | Type | Disp-MAE (°) | Disp-RMSE (°) | Disp-MARE | Depth-MAE (m) | Depth-RMSE (m) | Depth-MARE (m) | |
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|--------------------|----------------|--------------|---------------|-----------|---------------|----------------|----------------| |
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| [PSMNet](https://arxiv.org/abs/1803.08669) | Stereo | 0.33 | 0.54 | 0.20 | 2.79 | 6.17 | 0.29 | |
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| [360SD-Net](https://arxiv.org/abs/1911.04460) | 360° Stereo | 0.21 | 0.42 | 0.18 | 2.14 | 5.12 | 0.15 | |
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| [IGEV-Stereo](https://arxiv.org/abs/2303.06615) | Stereo | 0.22 | 0.41 | 0.17 | 1.85 | 4.44 | 0.15 | |
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| [360-IGEV-Stereo](https://arxiv.org/abs/2411.18335) | 360° Stereo | **0.18** | **0.39** | **0.15** | **1.77** | **4.36** | **0.14** | |
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## Project Page |
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For more information, visualizations, and updates, visit the **[project page](https://vita-epfl.github.io/Helvipad/)**. |
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## Citation |
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If you use the Helvipad dataset in your research, please cite our paper: |
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```bibtex |
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@misc{zayene2024helvipad, |
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author = {Zayene, Mehdi and Endres, Jannik and Havolli, Albias and Corbière, Charles and Cherkaoui, Salim and Ben Ahmed Kontouli, Alexandre and Alahi, Alexandre}, |
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title = {Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation}, |
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year = {2024}, |
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eprint = {2403.16999}, |
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archivePrefix = {arXiv}, |
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primaryClass = {cs.CV} |
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} |
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``` |
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## License |
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This dataset is licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). |
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## Acknowledgments |
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This work was supported by the [EPFL Center for Imaging](https://imaging.epfl.ch/) through a Collaborative Imaging Grant. |
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We thank the VITA lab members for their valuable feedback, which helped to enhance the quality of this manuscript. |
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We also express our gratitude to Dr. Simone Schaub-Meyer and Oliver Hahn for their insightful advice during the project's final stages. |
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