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README.md

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-license: mit
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+<div align="center">
+    <h2 align="center">The P<sup>3</sup> dataset: Pixels, Points and Polygons <br> for Multimodal Building Vectorization</h2>
+    <h3><align="center">Raphael Sulzer<sup>1,2</sup> &nbsp;&nbsp;&nbsp; Liuyun Duan<sup>1</sup>
+    &nbsp;&nbsp;&nbsp; Nicolas Girard<sup>1</sup>&nbsp;&nbsp;&nbsp; Florent Lafarge<sup>2</sup></a></h3>
+    <align="center"><sup>1</sup>LuxCarta Technology <br>  <sup>2</sup>Centre Inria d'Université Côte d'Azur
+    <img src="./teaser.jpg" width=100% height=100%>
+    <b>Figure 1</b>: A view of our dataset of Zurich, Switzerland
+</div>
+
+## Abstract:
+
+<div align="justify">
+We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. The dataset contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 cm. While many existing datasets primarily focus on the image modality, P<sup>3</sup> offers a complementary perspective by also incorporating dense 3D information. We demonstrate that LiDAR point clouds serve as a robust modality for predicting building polygons, both in hybrid and end-to-end learning frameworks. Moreover, fusing aerial LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P<sup>3</sup> dataset is publicly available, along with code and pretrained weights of three state-of-the-art models for building polygon prediction at https://github.com/raphaelsulzer/PixelsPointsPolygons.
+</div>
+
+## Highlights
+
+- A global, multimodal dataset of aerial images, aerial lidar point clouds and building polygons
+- A library for training and evaluating state-of-the-art deep learning methods on the dataset
+
+
+## Dataset
+
+### Download
+
+You can download the dataset at [huggingface.co/datasets/rsi/PixelsPointsPolygons](https://huggingface.co/datasets/rsi/PixelsPointsPolygons) .
+
+
+### Overview
+
+<div align="left">
+    <img src="./worldmap.jpg" width=60% height=50%>
+</div>
+
+
+<!-- ### Prepare custom tile size
+
+See [datasets preprocessing](data_preprocess) for instructions on preparing a dataset with different tile sizes. -->
+
+
+## Code 
+
+### Download
+
+```
+git clone https://github.com/raphaelsulzer/PixelsPointsPolygons
+```
+
+### Requirements
+
+To create a conda environment named `ppp` and install the repository as a python package with all dependencies run
+```
+bash install.sh
+```
+
+or, if you want to manage the environment yourself run
+```
+pip install -r requirements-torch-cuda.txt
+pip install .
+```
+⚠️ **Warning**: The implementation of the LiDAR point cloud encoder uses Open3D-ML. Currently, Open3D-ML officially only supports the PyTorch version specified in `requirements-torch-cuda.txt`.
+
+
+
+<!-- ## Model Zoo
+
+
+| Model                     | \<model>  | Encoder                   | \<encoder>            |Image  |LiDAR  | IoU       | C-IoU     |
+|---------------            |----       |---------------            |---------------        |---    |---    |-----      |-----       |
+| Frame Field Learning      |\<ffl>     | Vision Transformer (ViT)  | \<vit_cnn>            | ✅    |       | 0.85      | 0.90      |
+| Frame Field Learning      |\<ffl>     | PointPillars (PP) + ViT   | \<pp_vit_cnn>         |       | ✅    | 0.80      | 0.88      |
+| Frame Field Learning      |\<ffl>     | PP+ViT \& ViT             | \<fusion_vit_cnn>     | ✅    |✅     | 0.78      | 0.85      |
+| HiSup                     |\<hisup>   | Vision Transformer (ViT)  | \<vit_cnn>            | ✅    |       | 0.85      | 0.90      |
+| HiSup                     |\<hisup>   | PointPillars (PP) + ViT   | \<pp_vit_cnn>         |       | ✅    | 0.80      | 0.88      |
+| HiSup                     |\<hisup>   | PP+ViT \& ViT             | \<fusion_vit>         | ✅    |✅     | 0.78      | 0.85      |
+| Pix2Poly                  |\<pix2poly>| Vision Transformer (ViT)  | \<vit>                | ✅    |       | 0.85      | 0.90      |
+| Pix2Poly                  |\<pix2poly>| PointPillars (PP) + ViT   | \<pp_vit>             |       | ✅    | 0.80      | 0.88      |
+| Pix2Poly                  |\<pix2poly>| PP+ViT \& ViT             | \<fusion_vit>         | ✅    |✅     | 0.78      | 0.85      | -->
+
+### Configuration
+
+The project supports hydra configuration which allows to modify any parameter from the command line, such as the model and encoder types from the table above.
+To view all available options run
+```
+python train.py --help
+```
+
+### Training
+
+Start training with the following command:
+
+```
+torchrun --nproc_per_node=<num GPUs> train.py model=<model> encoder=<encoder> model.batch_size=<batch size> ...
+
+```
+
+### Prediction
+
+```
+torchrun --nproc_per_node=<num GPUs> predict.py model=<model> checkpoint=best_val_iou ...
+
+```
+
+### Evaluation
+
+```
+python evaluate.py model=<model> checkpoint=best_val_iou
+```
+<!-- ## Trained models
+
+asd -->
+
+
+<!-- ## Results
+
+#TODO Put paper main results table here -->
+
+
+## Citation
+
+If you find our work useful, please consider citing:
+```bibtex
+...
+```
+
+## Acknowledgements
+
+This repository benefits from the following open-source work. We thank the authors for their great work.
+
+1. [Frame Field Learning](https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning)
+2. [HiSup](https://github.com/SarahwXU/HiSup)
+3. [Pix2Poly](https://github.com/yeshwanth95/Pix2Poly)

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