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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- robotics |
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--- |
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# 360DVO Dataset |
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This is the official dataset from the paper [360DVO: Deep Visual Odometry for Monocular 360-Degree Camera](https://huggingface.co/papers/2601.02309), which is published on IEEE Robotics and Automation Letters (RA-L) 2026. |
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[Project Page](https://chris1004336379.github.io/360DVO-homepage) | [Paper](https://huggingface.co/papers/2601.02309) | [Code](https://github.com/chris1004336379/360DVO) |
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360DVO is a deep learning-based omnidirectional visual odometry (OVO) framework. It introduces a distortion-aware spherical feature extractor (DAS-Feat) and an omnidirectional differentiable bundle adjustment (ODBA) module. This repository provides the real-world OVO benchmark introduced in the study to facilitate evaluation in realistic settings. |
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## Updates |
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* **`Jan. 9th, 2026`**: we update the names of each sequence for better understanding. |
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* **`Jan. 6th, 2026`**: we add project page and arxiv paper link. |
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* **`Jan. 4th, 2026`**: we release the 360DVO dataset with ground truth trajectories. |
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## Details |
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360DVO Dataset is a large-scale real-world OVO benchmark emphasizing practical challenges across diverse environments and motions. It includes 20 sequences (~1k frames each) and all images are standardized to 3840x1920 at 10 FPS. Pseudo ground truth is reconstructed via SfM software Agisoft Metashape. |
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To facilitate rigorous evaluation, we partition the dataset into Easy and Hard subsets (10 each) by trajectory complexity and scene dynamics, from linear, static cases to aggressive rotations, lighting shifts, and dynamic occlusions. |
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| Sequence | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | |
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|:--------:|:------------:|:-----------:|:---------------:|:---------------:|:----------------:|:----------------:|:-------------:|:----------------:|:-------------------:|:--------------:| |
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| **Easy** | bridge night | canyon line | city driving | downhill biking | hongkong central | hongkong wanchai | mountains | shanghai driving | snowmobile | tokyo citywalk | |
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| **Hard** | canyon loop | dragon boat | drone racetrack | field | grove | indoor RC car | london bridge | ridge to lake | snowy mountain road | wingsuit | |
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* ## Usage |
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```python |
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# Authentication |
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from huggingface_hub import login |
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login() |
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# Download |
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from datasets import load_dataset |
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dataset = load_dataset("chris1004336379/360DVO") |
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``` |
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We also provide a script ```eval.py``` for evaluation. |
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* ## Citation Information |
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``` |
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@misc |
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{ |
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guo2026360dvodeepvisualodometry, |
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title={360DVO: Deep Visual Odometry for Monocular 360-Degree Camera}, |
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author={Xiaopeng Guo and Yinzhe Xu and Huajian Huang and Sai-Kit Yeung}, |
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year={2026}, |
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eprint={2601.02309}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2601.02309}, |
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} |
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``` |
