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---
license: other
license_name: license
license_link: LICENSE
---
# Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
<!-- ## [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) -->
<div align="center">
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</div>
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.
If you find this dataset helpful for your research, please cite our paper using the following reference:
```
@inproceedings{2023wildplaces,
title={Wild-places: A large-scale dataset for lidar place recognition in unstructured natural environments},
author={Knights, Joshua and Vidanapathirana, Kavisha and Ramezani, Milad and Sridharan, Sridha and Fookes, Clinton and Moghadam, Peyman},
booktitle={2023 IEEE international conference on robotics and automation (ICRA)},
pages={11322--11328},
year={2023},
organization={IEEE}
}
```
## Download Instructions
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.
## Training and Benchmarking
Here we provide pre-trained checkpoints and results for benchmarking several state-of-the-art LPR methods on the Wild-Places dataset.
**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.
### Checkpoints
|Release| Model | Checkpoint |
|------------|------------|------------|
|ICRA2023| TransLoc3D | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/TransLoc3D.pth) |
|ICRA2023| MinkLoc3DV2 | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/MinkLoc3Dv2.pth) |
|ICRA2023| LoGG3D-Net | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/ICRA_2023_checkpoints/LoGG3D-Net.pth) |
|2025 Re-Training| MinkLoc3DV2 | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/2025_updated_checkpoints/MinkLoc3Dv2.pth) |
|2025 Re-Training| LoGG3D-Net | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/2025_updated_checkpoints/LoGG3D-Net.pth)
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) |