Update README.md

README.md

@@ -1,20 +1,20 @@
 # CS-Wild-Places: Large-Scale 3D Dataset for Lidar Place Recognition Across Ground and Aerial Views
 
-The dataset is published as a contribution in our CVPR paper titled:
+The dataset is published as a contribution of our CVPR paper titled:
 **HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views**, CVPR 2025 by *Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, and Milad Ramezani*\
 [[**Website**](https://csiro-robotics.github.io/HOTFormerLoc)] <!-- [[**Paper**](https://cvpr.thecvf.com)] --> [[**arXiv**](https://arxiv.org/abs/2503.08140)] <!-- [[**Video**](https://youtube.com)] --> [[**CS-Wild-Places Dataset**](https://data.csiro.au/collection/csiro:64896)] [[**CS-Wild-Places README**](https://github.com/csiro-robotics/HOTFormerLoc/blob/main/media/CS_Wild_Places_README.pdf)]
 
-[**CS-Wild-Places**](https://data.csiro.au/collection/csiro:64896), a 3D cross-source dataset featuring point cloud data from aerial and ground lidar scans captured in four dense forests. Point clouds in CS-Wild-Places contain representational gaps and distinctive attributes such as varying point densities and noise patterns, making it a challenging benchmark for cross-view localisation in the wild.
+[**CS-Wild-Places**](https://data.csiro.au/collection/csiro:64896), is a 3D cross-source dataset featuring point cloud data from aerial and ground lidar scans captured in four dense forests. Point clouds in CS-Wild-Places contain representational gaps and distinctive attributes such as varying point densities and noise patterns, making it a challenging benchmark for cross-view localisation in the wild.
 
 ![CS-Wild-Places](media/CSWildPlaces_overview.png)
 *CS-Wild-Places dataset. (Top row) birds eye view of aerial global maps from all four forests. 
 (Bottom row) sample ground and aerial submap from each forest.*
 
-Our results demonstrate that HOTFormerLoc achieves a top-1 average recall improvement of 5.5% – 11.5% on the CS-Wild-Places benchmark. Furthermore, it consistently outperforms SOTA 3D place recognition methods, with an average performance gain of 4.9% on well established urban and forest datasets. 
-
+In our paper, we demonstrated that HOTFormerLoc achieves a top-1 average recall improvement of 5.5% – 11.5% on the CS-Wild-Places benchmark. Furthermore, it consistently outperforms SOTA 3D place recognition methods, with an average performance gain of 4.9% on well established urban and forest datasets. Please check out our paper for more information.
 <!-- ![Hero Figure](media/radar_plot.svg) -->
 <img src="media/radar_plot.svg" alt="Hero Figure" width="50%" height="auto" style="display: block; margin: auto;">
 
+**Note: If you use our dataset please cite the folowing paper.**
 ### Citation
 If you find this work useful, please consider citing:
 ```
@@ -29,57 +29,7 @@ If you find this work useful, please consider citing:
 <!-- month     = {todo},
 pages     = {todo} -->
 
-## Environment and Dependencies
-Code was tested using Python 3.11 with PyTorch 2.1.1 and CUDA 12.1 on a Linux system. We use conda to manage dependencies (although we recommend [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) for a much faster install).
-
-### Installation
-```
-# Note: replace 'mamba' with 'conda' if using a vanilla conda install
-mamba create -n hotformerloc python=3.11 -c conda-forge
-mamba activate hotformerloc
-mamba install 'numpy<2.0' -c conda-forge
-mamba install pytorch==2.1.1 torchvision==0.16.1 pytorch-cuda=12.1 -c pytorch -c nvidia -c conda-forge
-pip install -r requirements.txt
-pip install libs/dwconv
-```
-
-Modify the `PYTHONPATH` environment variable to include the absolute path to the repository root folder (ensure this variable is set every time you open a new shell): 
-```export PYTHONPATH
-export PYTHONPATH=$PYTHONPATH:<path/to/HOTFormerLoc>
-```
-
-## Datasets
-
-### Wild-Places
-We train on the Wild-Places dataset introduced in *Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments* ([link](https://arxiv.org/pdf/2211.12732)).
-
-Download the dataset [here](https://csiro-robotics.github.io/Wild-Places/#8-Download), and place or symlink the data in `data/wild_places`.
-
-Run the following to fix the broken timestamps in the poses files:
-```
-cd datasets/WildPlaces
-python fix_broken_timestamps.py \
-	--root '../../data/wild_places/data/' \
-	--csv_filename 'poses.csv' \
-	--csv_savename 'poses_fixed.csv' \
-	--cloud_folder 'Clouds'
-
-python fix_broken_timestamps.py \
-	--root '../../data/wild_places/data/' \
-	--csv_filename 'poses_aligned.csv' \
-	--csv_savename 'poses_aligned_fixed.csv' \
-	--cloud_folder 'Clouds_downsampled'
-```
-
-Before network training or evaluation, run the below code to generate pickles with positive and negative point clouds for each anchor point cloud:
-```
-cd datasets/WildPlaces
-python generate_training_tuples.py \
-	--root '../../data/wild_places/data/' 
 
-python generate_test_sets.py \
-	--root '../../data/wild_places/data/'
-```
 
 ### CS-Wild-Places
 We train on our novel CS-Wild-Places dataset, introduced in further detail in our [paper](https://arxiv.org/abs/2503.08140). CS-Wild-Places is built upon the ground traversals introduced by Wild-Places, so it is required to download the Wild-Places dataset alongside our data following the instructions in the above section (generating train/test pickles for Wild-Places is not required for CS-Wild-Places, so this step can be skipped). Note that the full Wild-Places dataset must be downloaded as our post-processing utilises the full resolution submaps.
@@ -116,117 +66,7 @@ python generate_train_test_tuples.py \
 ```
 Note that training and evaluation pickles are saved to the directory specified in `--root` by default. 
 
-### CS-Campus3D
-We train on the CS-Campus3D dataset introduced in *CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition* ([link](https://arxiv.org/pdf/2303.17778)).
-
-Download the dataset [here](https://drive.google.com/file/d/1yxVicykRMg_HAfZG2EQUl1R3_wxpxStd/view?usp=sharing), and place or symlink the data in `data/benchmark_datasets_cs_campus3d`. 
-
-Run the below commands to convert the CS_Campus3D train and test pickles into a suitable format for use with HOTFormerLoc.
-
-```
-cd datasets/CSCampus3D
-python save_queries_HOTFormerLoc_format.py
-```
-
-### Oxford RobotCar
-We trained on a subset of Oxford RobotCar and the In-house (U.S., R.A., B.D.) datasets introduced in
-*PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition* ([link](https://arxiv.org/pdf/1804.03492)).
-There are two training datasets:
-- Baseline Dataset - consists of a training subset of Oxford RobotCar
-- Refined Dataset - consists of training subset of Oxford RobotCar and training subset of In-house
-
-We report results on the Baseline set in the paper.
-
-For dataset description see the PointNetVLAD paper or github repository ([link](https://github.com/mikacuy/pointnetvlad)).
-
-You can download the dataset from 
-[here](https://drive.google.com/open?id=1rflmyfZ1v9cGGH0RL4qXRrKhg-8A-U9q) 
-([alternative link](https://drive.google.com/file/d/1-1HA9Etw2PpZ8zHd3cjrfiZa8xzbp41J/view?usp=sharing)), then place or symlink the data in `data/benchmark_datasets`.
-
-Before network training or evaluation, run the below code to generate pickles with positive and negative point clouds for each anchor point cloud. 
-
-```generate pickles
-cd datasets/pointnetvlad 
-
-# Generate training tuples for the Baseline Dataset
-python generate_training_tuples_baseline.py --dataset_root '../../data/benchmark_datasets'
-
-# (Optionally) Generate training tuples for the Refined Dataset
-python generate_training_tuples_refine.py --dataset_root '../../data/benchmark_datasets'
-
-# Generate evaluation tuples
-python generate_test_sets.py --dataset_root '../../data/benchmark_datasets'
-```
-
-## Training
-To train **HOTFormerLoc**, download the datasets and generate training pickles as described above for any dataset you wish to train on. 
-The configuration files for each dataset can be found in `config/`. 
-Set the `dataset_folder` parameter to the dataset root folder (only necessary if you have issues with the default relative path).
-If running out of GPU memory, decrease `batch_split_size` and `val_batch_size` parameter value. If running out of RAM, you may need to decrease the `batch_size` parameter or try reducing `num_workers` to 1, but note that a smaller batch size may slightly reduce performance. We use wandb for logging by default, but this can be disabled in the config.
-
-To train the network, run:
-
-```
-cd training
-
-# To train HOTFormerLoc on CS-Wild-Places
-python train.py --config ../config/config_cs-wild-places.txt --model_config ../models/hotformerloc_cs-wild-places_cfg.txt
-
-# To train HOTFormerLoc on Wild-Places
-python train.py --config ../config/config_wild-places.txt --model_config ../models/hotformerloc_wild-places_cfg.txt
-
-# To train HOTFormerLoc on CS-Campus3D
-python train.py --config ../config/config_cs-campus3d.txt --model_config ../models/hotformerloc_cs-campus3d_cfg.txt
-
-# To train HOTFormerLoc on Oxford RobotCar
-python train.py --config ../config/config_oxford.txt --model_config ../models/hotformerloc_oxford_cfg.txt
-```
-
-If training on a SLURM cluster, we provide the `submitit_train_job_single_node.py` script to automate training job submission, with support for automatic checkpointing and resubmission on job timeout. Make sure to set job parameters appropriately for your cluster.
-
-### Pre-trained Weights
-
-Pre-trained weights for HOTFormerLoc and other experiments can be downloaded and placed in the `weights` directory. You can download them individually below, or download and extract all from [this link](https://www.dropbox.com/scl/fi/qjyh966styqlye38a4c37/pretrained_weights.tar.gz?rlkey=qkuhupf3og7mfkfid8dts7xej&st=wx8q2v68&dl=0).
-| Model        | Dataset         | Weights Download                                                                                                                                                         |
-|--------------|-----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| HOTFormerLoc | CS-Wild-Places  | [hotformerloc_cs-wild-places.pth](https://www.dropbox.com/scl/fi/bcgcmbyic591f3bviib64/hotformerloc_cs-wild-places.pth?rlkey=vrw0seq6nfbsihijbhqatll2u&st=d7enawjw&dl=0) |
-| HOTFormerLoc | CS-Campus3D     | [hotformerloc_cs-campus3D.pth](https://www.dropbox.com/scl/fi/l9jyn5310gjf80zw35v7z/hotformerloc_cs-campus3d.pth?rlkey=s0bpcysyc1xt2357shhclpnlw&st=zhh679b9&dl=0)       |
-| HOTFormerLoc | Wild-Places     | [hotformerloc_wild-places.pth](https://www.dropbox.com/scl/fi/yd94iy9dq6k1m312ifnyx/hotformerloc_wild-places.pth?rlkey=5ndv0p48c7hyjvah90eab1l1e&st=zl1716hh&dl=0)       |
-| HOTFormerLoc | Oxford RobotCar | [hotformerloc_oxford.pth](https://www.dropbox.com/scl/fi/4r3470zo9zomkyjys5nrm/hotformerloc_oxford.pth?rlkey=eocfo3yvmhuqqgsmjtypgf78s&st=ybhzcj6y&dl=0)                 |
-| MinkLoc3Dv2  | CS-Wild-Places  | [minkloc3dv2_cs-wild-places.pth](https://www.dropbox.com/scl/fi/2w4l8gv7qbmp0lh4eztsf/minkloc3dv2_cs-wild-places.pth?rlkey=udxvtkr6yfgdnyizra4gmw0qa&st=p0evrh61&dl=0)   |
-| CrossLoc3D   | CS-Wild-Places  | [crossloc3d_cs-wild-places.pth](https://www.dropbox.com/scl/fi/5ikt1jvr2fabiaw8mhqbb/crossloc3d_cs-wild-places.pth?rlkey=lb4gp2n814im3twy4zy5d67bd&st=znup5ewi&dl=0)     |
-| LoGG3D-Net   | CS-Wild-Places  | [logg3dnet_cs-wild-places.pth](https://www.dropbox.com/scl/fi/51se5akdyg35xy2dsrosj/logg3dnet_cs-wild-places.pth?rlkey=4nvvp8gw656wdbj3081jzcn0i&st=n5ytpnzc&dl=0)       |
-
-## Evaluation
-
-To evaluate the pretrained models run the following commands:
-
-```
-cd eval
-
-# To evaluate HOTFormerLoc trained on CS-Wild-Places
-python pnv_evaluate.py --config ../config/config_cs-wild-places.txt --model_config ../models/hotformerloc_cs-wild-places_cfg.txt --weights ../weights/hotformerloc_cs-wild-places.pth
-
-# To evaluate HOTFormerLoc trained on Wild-Places
-python pnv_evaluate.py --config ../config/config_wild-places.txt --model_config ../models/hotformerloc_wild-places_cfg.txt --weights ../weights/hotformerloc_wild-places.pth
-
-# To evaluate HOTFormerLoc trained on CS-Campus3D
-python pnv_evaluate.py --config ../config/config_cs-campus3d.txt --model_config ../models/hotformerloc_cs-campus3d_cfg.txt --weights ../weights/hotformerloc_cs-campus3d.pth
-
-# To evaluate HOTFormerLoc trained on Oxford RobotCar
-python pnv_evaluate.py --config ../config/config_oxford.txt --model_config ../models/hotformerloc_oxford_cfg.txt --weights ../weights/hotformerloc_oxford.pth
-```
-
-Below are the results for all evaluated models on CS-Wild-Places:
-
-![CS-Wild-Places_baseline](media/dataset_cswp_baseline.png)
-*Comparison of SOTA on CS-Wild-Places Baseline evaluation set.*
-
-![CS-Wild-Places_unseen](media/dataset_cswp_unseen.png)
-*Comparison of SOTA on CS-Wild-Places Unseen evaluation set.*
-
-See the paper for full results and comparison with SOTA on all datasets.
 
 ## Acknowledgements
 
-Special thanks to the authors of [MinkLoc3Dv2](https://github.com/jac99/MinkLoc3Dv2) and [OctFormer](https://github.com/octree-nn/octformer) for their excellent code, which formed the foundation of this codebase. We would also like to thank the authors of [Wild-Places](https://csiro-robotics.github.io/Wild-Places/) for their fantastic dataset which serves as the base that CS-Wild-Places is built upon.
+We would also like to thank the authors of [Wild-Places](https://csiro-robotics.github.io/Wild-Places/) for their fantastic dataset which serves as the base that CS-Wild-Places is built upon.