Update README.md

README.md

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-license: other
-license_name: license
-license_link: LICENSE
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+# 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">
+<a href="https://arxiv.org/abs/2211.12732"><img src='https://img.shields.io/badge/arXiv-Wild Places-red' alt='Paper PDF'></a>
+<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>
+<a href='https://huggingface.co/CSIRORobotics/Wild-Places'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoints-yellow'></a>
+<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>
+</div>
+
+
+This repository contains the code implementation used 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}
+}
+```
+
+## Contents
+1. [Updates](#updates)
+2. [Download Instructions](#download-instructions)
+3. [Benchmarking](#benchmarking)
+    * [Checkpoints](#checkpoints)
+    * [Performance](#performance)
+4. [Scripts](#scripts)
+    * [Loading Point Clouds](#loading-point-clouds)
+    * [Training](#training)
+    * [Evaluation](#evaluation)
+4. [Thanks](#thanks)
+
+## Updates 
+- **Oct 2022** Wild-Places v1.0 Uploaded
+- **Jan 2023** Wild-Places is accepted to ICRA2023!
+- **Jan 2023** Wild-Places v2.0 Uploaded.  This update to the dataset integrates GPS into the SLAM solution to alleviate vertical drift in the larger loops of the traversal in both environments. NOTE: Sequence K-04 is currently unavailable for v2.0 due to a failed loop closure in the ground truth.  We are currently working on remedying this, and will release the sequence as soon this issue is rectified.
+- **Feb 2025** Fix the broken timestamps in the poses files. 
+- **Nov 2025** Wild-Places v3.0 Uploaded.  This update to the dataset includes:
+    - Updated point clouds / trajectories using the latest version of WildCat to bring the dataset in line with the pointclouds available in the WildScenes and WildCross datasets
+    - Updated benchmarking results and instructions for training on LoGG3D-Net and MinkLoc3Dv2 
+    - Updated dataset and repository file structure
+
+
+## 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. 
+
+
+## Benchmarking
+Here we provided 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)       |
+
+
+### Performance
+![](./utils/docs/nov2025_wildplaces_benchmarking_results.png)
+
+## Scripts
+### Loading Point Clouds
+A code snippet to load a pointcloud file from our dataset can be found in `eval/load_pointcloud.py`
+### Training
+We provide instructions for how to add Wild-Places as a training dataset for two state-of-the-art LPR methods: [LoGG3D-Net](https://github.com/csiro-robotics/LoGG3D-Net) and [MinkLoc3Dv2](https://github.com/jac99/MinkLoc3Dv2).  For more detailed instructions, please consult the `README.md` files in `training/LoGG3D-Net` and `training/MinkLoc3Dv2`.
+### Evaluation
+We provide generic evaluation code for evaluating performance on the Wild-Places dataset for both the inter and intra-sequence testing scenarios, as well as an implementation of ScanContext.  For more details, please see the `README.md` file in the `eval` folder.
+
+## Thanks
+Special thanks to the authors of the [PointNetVLAD](https://github.com/mikacuy/pointnetvlad) and [MinkLoc3D](https://github.com/jac99/MinkLoc3D), whose excellent code was used as a basis for the generation and evaluation scripts used in this repository.