Update README.md

README.md

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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
+![](https://raw.githubusercontent.com/csiro-robotics/Wild-Places/refs/heads/main/utils/docs/teaser_image.png)
 <!-- ## [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>
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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>
 </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.  
+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:
 ```
@@ -22,35 +28,11 @@ If you find this dataset helpful for your research, please cite our paper using
 }
 ```
 
-## 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
+## Training and 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.
@@ -62,19 +44,7 @@ Here we provided pre-trained checkpoints and results for benchmarking several st
 |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)
+|2025 Re-Training| LoGG3D-Net  | [Link](https://huggingface.co/CSIRORobotics/Wild-Places/resolve/main/2025_updated_checkpoints/LoGG3D-Net.pth)       
 
-## 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. 
+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)