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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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- image-to-3d |
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tags: |
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- 3d-reconstruction |
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- 3d-tracking |
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- point-cloud |
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- dynamic-scenes |
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- depth-estimation |
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- pose-estimation |
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--- |
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# POMATO: PointOdyssey Tracking Data |
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This repository contains the processed **PointOdyssey** tracking data used in the paper [POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction](https://huggingface.co/papers/2504.05692). |
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The **POMATO** framework proposes a unified approach for dynamic 3D reconstruction by marrying pointmap matching with temporal motion. It enables robust performance across various downstream tasks, including video depth estimation, 3D point tracking, and pose estimation. This dataset specifically supports the 3D point tracking evaluation mentioned in the paper. |
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## Code |
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The official code repository for POMATO can be found at: [https://github.com/wyddmw/POMA_eval](https://github.com/wyddmw/POMA_eval) |
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## Dataset Usage |
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This repository provides the processed **PointOdyssey** data, specifically for tracking evaluation as mentioned in the official [POMATO GitHub repository's Tracking section](https://github.com/wyddmw/POMA_eval#tracking). |
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To download the data: |
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```bash |
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huggingface-cli download xiaochui/POMATO_Tracking po_seq.zip --local-dir ./data/tracking_eval_data |
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``` |
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After downloading, unzip it to `data/tracking_eval_data`: |
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```bash |
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cd data/tracking_eval_data |
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unzip po_seq.zip |
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``` |
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For more details on how to use this data for tracking experiments and evaluation, refer to the [official POMATO GitHub repository's Tracking section](https://github.com/wyddmw/POMA_eval#tracking). |
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## Citation |
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If you find our POMATO work, including this dataset, useful in your research or applications, please consider citing using the following BibTeX: |
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```bibtex |
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@article{zhang2025pomato, |
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title={POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction}, |
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author={Zhang, Songyan and Ge, Yongtao and Tian, Jinyuan and Xu, Guangkai and Chen, Hao and Lv, Chen and Shen, Chunhua}, |
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journal={arXiv preprint arXiv:2504.05692}, |
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year={2025} |
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} |
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``` |
