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--- |
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license: other |
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license_name: license |
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license_link: LICENSE |
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--- |
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# Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments |
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<!-- ## [Website](https://csiro-robotics.github.io/Wild-Places/) | [Paper](https://arxiv.org/abs/2211.12732) | [Data Download Portal](https://data.csiro.au/collection/csiro:56372?q=wild-places&_st=keyword&_str=1&_si=1) --> |
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<div align="center"> |
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<a href="https://arxiv.org/abs/2211.12732"><img src='https://img.shields.io/badge/arXiv-Wild Places-red' alt='Paper PDF'></a> |
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<a href='https://csiro-robotics.github.io/Wild-Places/'><img src='https://img.shields.io/badge/Project_Page-Wild Places-green' alt='Project Page'></a> |
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<a href='https://huggingface.co/CSIRORobotics/Wild-Places'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoints-yellow'></a> |
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<a href='https://data.csiro.au/collection/csiro:56372?q=wild-places&_st=keyword&_str=1&_si=1'><img src='https://img.shields.io/badge/Download-Wild Places-blue' alt='Project Page'></a> |
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</div> |
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This repository contains pre-trained checkpoints for the dataset introduced in the paper *Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments*, which has been published at ICRA2023. |
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If you find this dataset helpful for your research, please cite our paper using the following reference: |
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``` |
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@inproceedings{2023wildplaces, |
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title={Wild-places: A large-scale dataset for lidar place recognition in unstructured natural environments}, |
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author={Knights, Joshua and Vidanapathirana, Kavisha and Ramezani, Milad and Sridharan, Sridha and Fookes, Clinton and Moghadam, Peyman}, |
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booktitle={2023 IEEE international conference on robotics and automation (ICRA)}, |
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pages={11322--11328}, |
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year={2023}, |
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organization={IEEE} |
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} |
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``` |
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## Download Instructions |
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Our dataset can be downloaded through [The CSIRO Data Access Portal](https://data.csiro.au/collection/csiro:56372?q=wild-places&_st=keyword&_str=1&_si=1). Detailed instructions for downloading the dataset can be found in the README file provided on the data access portal page. |
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## Training and Benchmarking |
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Here we provide pre-trained checkpoints and results for benchmarking several state-of-the-art LPR methods on the Wild-Places dataset. |
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**Update Nov. 2025**: With the release of Wild-Places v3.0, we have also re-run training for two state-of-the-art methods (LoGG3D-Net, MinkLoc3Dv2) on the Wild-Places dataset using expanded batch sizes to provide new training checkpoints which better reflect the capabilities of recent state-of-the-art GPUs. We provide checkpoints and benchmarked results for both the recently trained models and the checkpoints released with the ICRA2023 paper. |
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### Checkpoints |
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|Release| Model | Checkpoint | |
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|------------|------------|------------| |
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|ICRA2023| TransLoc3D | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/TransLoc3D.pth) | |
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|ICRA2023| MinkLoc3DV2 | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/MinkLoc3Dv2.pth) | |
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|ICRA2023| LoGG3D-Net | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/LoGG3D-Net.pth) | |
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|2025 Re-Training| MinkLoc3DV2 | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/2025_updated_checkpoints/MinkLoc3Dv2.pth) | |
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|2025 Re-Training| LoGG3D-Net | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/2025_updated_checkpoints/LoGG3D-Net.pth) |
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For further instructions on training and evaluating these checkpoints on the Wild-Places dataset, please follow the instructions found at the [Wild-Places GitHub](https://github.com/csiro-robotics/Wild-Places) |
