| --- |
| license: cc-by-4.0 |
| pretty_name: Pixels Point Polygons |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - image-segmentation |
| - object-detection |
| tags: |
| - Aerial |
| - Environement |
| - Multimodal |
| - Earth Observation |
| - Image |
| - Lidar |
| - ALS |
| - pointcloud |
| - Building |
| - Polygon |
| - Vectorization |
| language: |
| - en |
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| --- |
| |
| <div align="center"> |
| <h1 align="center">The P<sup>3</sup> Dataset: Pixels, Points and Polygons <br> for Multimodal Building Vectorization</h1> |
| <h3><align="center">Raphael Sulzer<sup>1,2</sup> Liuyun Duan<sup>1</sup> |
| Nicolas Girard<sup>1</sup> 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> |
| |
| ## Table of Contents |
|
|
| - [Abstract](#abstract) |
| - [Highlights](#highlights) |
| - [Dataset](#dataset) |
| - [Pretrained model weights](#pretrained-model-weights) |
| - [Code](#code) |
| - [Citation](#citation) |
| - [Acknowledgements](#acknowledgements) |
|
|
| ## 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 outline polygons, available at [huggingface.co/datasets/rsi/PixelsPointsPolygons](https://huggingface.co/datasets/rsi/PixelsPointsPolygons) |
| - A library for training and evaluating state-of-the-art deep learning methods on the dataset, available at [github.com/raphaelsulzer/PixelsPointsPolygons](https://github.com/raphaelsulzer/PixelsPointsPolygons) |
| - Pretrained model weights, available at [huggingface.co/rsi/PixelsPointsPolygons](https://huggingface.co/rsi/PixelsPointsPolygons) |
| - A paper with an extensive experimental validation, available at [arxiv.org/abs/2505.15379](https://arxiv.org/abs/2505.15379) |
|
|
| ## Dataset |
|
|
| ### Overview |
|
|
| <div align="left"> |
| <img src="./worldmap.jpg" width=60% height=50%> |
| </div> |
| |
| ### Download |
|
|
| The recommended and fastest way to download the dataset is to run |
|
|
| ``` |
| pip install huggingface_hub |
| python scripts/download_dataset.py --dataset-root $DATA_ROOT |
| ``` |
|
|
| Optionally you can also download the dataset by running |
|
|
| ``` |
| git lfs install |
| git clone https://huggingface.co/datasets/rsi/PixelsPointsPolygons $DATA_ROOT |
| ``` |
|
|
| Both options will download the full dataset, including aerial images (as .tif), aerial lidar point clouds (as .copc.laz) and building polygon annotaions (as MS-COCO .json) into `$DATA_ROOT` . The size of the dataset is around 163GB. |
|
|
| ### Structure |
|
|
| <details> |
| <summary>📁 Click to expand dataset folder structure</summary --> |
|
|
| ```text |
| PixelsPointsPolygons/data/224 |
| ├── annotations |
| │ ├── annotations_all_test.json |
| │ ├── annotations_all_train.json |
| │ └── annotations_all_val.json |
| │ ... (24 files total) |
| ├── images |
| │ ├── train |
| │ │ ├── CH |
| │ │ │ ├── 0 |
| │ │ │ │ ├── image0_CH_train.tif |
| │ │ │ │ ├── image1000_CH_train.tif |
| │ │ │ │ └── image1001_CH_train.tif |
| │ │ │ │ ... (5000 files total) |
| │ │ │ ├── 5000 |
| │ │ │ │ ├── image5000_CH_train.tif |
| │ │ │ │ ├── image5001_CH_train.tif |
| │ │ │ │ └── image5002_CH_train.tif |
| │ │ │ │ ... (5000 files total) |
| │ │ │ └── 10000 |
| │ │ │ ├── image10000_CH_train.tif |
| │ │ │ ├── image10001_CH_train.tif |
| │ │ │ └── image10002_CH_train.tif |
| │ │ │ ... (5000 files total) |
| │ │ │ ... (11 dirs total) |
| │ │ ├── NY |
| │ │ │ ├── 0 |
| │ │ │ │ ├── image0_NY_train.tif |
| │ │ │ │ ├── image1000_NY_train.tif |
| │ │ │ │ └── image1001_NY_train.tif |
| │ │ │ │ ... (5000 files total) |
| │ │ │ ├── 5000 |
| │ │ │ │ ├── image5000_NY_train.tif |
| │ │ │ │ ├── image5001_NY_train.tif |
| │ │ │ │ └── image5002_NY_train.tif |
| │ │ │ │ ... (5000 files total) |
| │ │ │ └── 10000 |
| │ │ │ ├── image10000_NY_train.tif |
| │ │ │ ├── image10001_NY_train.tif |
| │ │ │ └── image10002_NY_train.tif |
| │ │ │ ... (5000 files total) |
| │ │ │ ... (11 dirs total) |
| │ │ └── NZ |
| │ │ ├── 0 |
| │ │ │ ├── image0_NZ_train.tif |
| │ │ │ ├── image1000_NZ_train.tif |
| │ │ │ └── image1001_NZ_train.tif |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_NZ_train.tif |
| │ │ │ ├── image5001_NZ_train.tif |
| │ │ │ └── image5002_NZ_train.tif |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_NZ_train.tif |
| │ │ ├── image10001_NZ_train.tif |
| │ │ └── image10002_NZ_train.tif |
| │ │ ... (5000 files total) |
| │ │ ... (11 dirs total) |
| │ ├── val |
| │ │ ├── CH |
| │ │ │ └── 0 |
| │ │ │ ├── image0_CH_val.tif |
| │ │ │ ├── image100_CH_val.tif |
| │ │ │ └── image101_CH_val.tif |
| │ │ │ ... (529 files total) |
| │ │ ├── NY |
| │ │ │ └── 0 |
| │ │ │ ├── image0_NY_val.tif |
| │ │ │ ├── image100_NY_val.tif |
| │ │ │ └── image101_NY_val.tif |
| │ │ │ ... (529 files total) |
| │ │ └── NZ |
| │ │ └── 0 |
| │ │ ├── image0_NZ_val.tif |
| │ │ ├── image100_NZ_val.tif |
| │ │ └── image101_NZ_val.tif |
| │ │ ... (529 files total) |
| │ └── test |
| │ ├── CH |
| │ │ ├── 0 |
| │ │ │ ├── image0_CH_test.tif |
| │ │ │ ├── image1000_CH_test.tif |
| │ │ │ └── image1001_CH_test.tif |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_CH_test.tif |
| │ │ │ ├── image5001_CH_test.tif |
| │ │ │ └── image5002_CH_test.tif |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_CH_test.tif |
| │ │ ├── image10001_CH_test.tif |
| │ │ └── image10002_CH_test.tif |
| │ │ ... (4400 files total) |
| │ ├── NY |
| │ │ ├── 0 |
| │ │ │ ├── image0_NY_test.tif |
| │ │ │ ├── image1000_NY_test.tif |
| │ │ │ └── image1001_NY_test.tif |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_NY_test.tif |
| │ │ │ ├── image5001_NY_test.tif |
| │ │ │ └── image5002_NY_test.tif |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_NY_test.tif |
| │ │ ├── image10001_NY_test.tif |
| │ │ └── image10002_NY_test.tif |
| │ │ ... (4400 files total) |
| │ └── NZ |
| │ ├── 0 |
| │ │ ├── image0_NZ_test.tif |
| │ │ ├── image1000_NZ_test.tif |
| │ │ └── image1001_NZ_test.tif |
| │ │ ... (5000 files total) |
| │ ├── 5000 |
| │ │ ├── image5000_NZ_test.tif |
| │ │ ├── image5001_NZ_test.tif |
| │ │ └── image5002_NZ_test.tif |
| │ │ ... (5000 files total) |
| │ └── 10000 |
| │ ├── image10000_NZ_test.tif |
| │ ├── image10001_NZ_test.tif |
| │ └── image10002_NZ_test.tif |
| │ ... (4400 files total) |
| ├── lidar |
| │ ├── train |
| │ │ ├── CH |
| │ │ │ ├── 0 |
| │ │ │ │ ├── lidar0_CH_train.copc.laz |
| │ │ │ │ ├── lidar1000_CH_train.copc.laz |
| │ │ │ │ └── lidar1001_CH_train.copc.laz |
| │ │ │ │ ... (5000 files total) |
| │ │ │ ├── 5000 |
| │ │ │ │ ├── lidar5000_CH_train.copc.laz |
| │ │ │ │ ├── lidar5001_CH_train.copc.laz |
| │ │ │ │ └── lidar5002_CH_train.copc.laz |
| │ │ │ │ ... (5000 files total) |
| │ │ │ └── 10000 |
| │ │ │ ├── lidar10000_CH_train.copc.laz |
| │ │ │ ├── lidar10001_CH_train.copc.laz |
| │ │ │ └── lidar10002_CH_train.copc.laz |
| │ │ │ ... (5000 files total) |
| │ │ │ ... (11 dirs total) |
| │ │ ├── NY |
| │ │ │ ├── 0 |
| │ │ │ │ ├── lidar0_NY_train.copc.laz |
| │ │ │ │ ├── lidar10_NY_train.copc.laz |
| │ │ │ │ └── lidar1150_NY_train.copc.laz |
| │ │ │ │ ... (1071 files total) |
| │ │ │ ├── 5000 |
| │ │ │ │ ├── lidar5060_NY_train.copc.laz |
| │ │ │ │ ├── lidar5061_NY_train.copc.laz |
| │ │ │ │ └── lidar5062_NY_train.copc.laz |
| │ │ │ │ ... (2235 files total) |
| │ │ │ └── 10000 |
| │ │ │ ├── lidar10000_NY_train.copc.laz |
| │ │ │ ├── lidar10001_NY_train.copc.laz |
| │ │ │ └── lidar10002_NY_train.copc.laz |
| │ │ │ ... (4552 files total) |
| │ │ │ ... (11 dirs total) |
| │ │ └── NZ |
| │ │ ├── 0 |
| │ │ │ ├── lidar0_NZ_train.copc.laz |
| │ │ │ ├── lidar1000_NZ_train.copc.laz |
| │ │ │ └── lidar1001_NZ_train.copc.laz |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── lidar5000_NZ_train.copc.laz |
| │ │ │ ├── lidar5001_NZ_train.copc.laz |
| │ │ │ └── lidar5002_NZ_train.copc.laz |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── lidar10000_NZ_train.copc.laz |
| │ │ ├── lidar10001_NZ_train.copc.laz |
| │ │ └── lidar10002_NZ_train.copc.laz |
| │ │ ... (4999 files total) |
| │ │ ... (11 dirs total) |
| │ ├── val |
| │ │ ├── CH |
| │ │ │ └── 0 |
| │ │ │ ├── lidar0_CH_val.copc.laz |
| │ │ │ ├── lidar100_CH_val.copc.laz |
| │ │ │ └── lidar101_CH_val.copc.laz |
| │ │ │ ... (529 files total) |
| │ │ ├── NY |
| │ │ │ └── 0 |
| │ │ │ ├── lidar0_NY_val.copc.laz |
| │ │ │ ├── lidar100_NY_val.copc.laz |
| │ │ │ └── lidar101_NY_val.copc.laz |
| │ │ │ ... (529 files total) |
| │ │ └── NZ |
| │ │ └── 0 |
| │ │ ├── lidar0_NZ_val.copc.laz |
| │ │ ├── lidar100_NZ_val.copc.laz |
| │ │ └── lidar101_NZ_val.copc.laz |
| │ │ ... (529 files total) |
| │ └── test |
| │ ├── CH |
| │ │ ├── 0 |
| │ │ │ ├── lidar0_CH_test.copc.laz |
| │ │ │ ├── lidar1000_CH_test.copc.laz |
| │ │ │ └── lidar1001_CH_test.copc.laz |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── lidar5000_CH_test.copc.laz |
| │ │ │ ├── lidar5001_CH_test.copc.laz |
| │ │ │ └── lidar5002_CH_test.copc.laz |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── lidar10000_CH_test.copc.laz |
| │ │ ├── lidar10001_CH_test.copc.laz |
| │ │ └── lidar10002_CH_test.copc.laz |
| │ │ ... (4400 files total) |
| │ ├── NY |
| │ │ ├── 0 |
| │ │ │ ├── lidar0_NY_test.copc.laz |
| │ │ │ ├── lidar1000_NY_test.copc.laz |
| │ │ │ └── lidar1001_NY_test.copc.laz |
| │ │ │ ... (4964 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── lidar5000_NY_test.copc.laz |
| │ │ │ ├── lidar5001_NY_test.copc.laz |
| │ │ │ └── lidar5002_NY_test.copc.laz |
| │ │ │ ... (4953 files total) |
| │ │ └── 10000 |
| │ │ ├── lidar10000_NY_test.copc.laz |
| │ │ ├── lidar10001_NY_test.copc.laz |
| │ │ └── lidar10002_NY_test.copc.laz |
| │ │ ... (4396 files total) |
| │ └── NZ |
| │ ├── 0 |
| │ │ ├── lidar0_NZ_test.copc.laz |
| │ │ ├── lidar1000_NZ_test.copc.laz |
| │ │ └── lidar1001_NZ_test.copc.laz |
| │ │ ... (5000 files total) |
| │ ├── 5000 |
| │ │ ├── lidar5000_NZ_test.copc.laz |
| │ │ ├── lidar5001_NZ_test.copc.laz |
| │ │ └── lidar5002_NZ_test.copc.laz |
| │ │ ... (5000 files total) |
| │ └── 10000 |
| │ ├── lidar10000_NZ_test.copc.laz |
| │ ├── lidar10001_NZ_test.copc.laz |
| │ └── lidar10002_NZ_test.copc.laz |
| │ ... (4400 files total) |
| └── ffl |
| ├── train |
| │ ├── CH |
| │ │ ├── 0 |
| │ │ │ ├── image0_CH_train.pt |
| │ │ │ ├── image1000_CH_train.pt |
| │ │ │ └── image1001_CH_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_CH_train.pt |
| │ │ │ ├── image5001_CH_train.pt |
| │ │ │ └── image5002_CH_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_CH_train.pt |
| │ │ ├── image10001_CH_train.pt |
| │ │ └── image10002_CH_train.pt |
| │ │ ... (5000 files total) |
| │ │ ... (11 dirs total) |
| │ ├── NY |
| │ │ ├── 0 |
| │ │ │ ├── image0_NY_train.pt |
| │ │ │ ├── image1000_NY_train.pt |
| │ │ │ └── image1001_NY_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_NY_train.pt |
| │ │ │ ├── image5001_NY_train.pt |
| │ │ │ └── image5002_NY_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_NY_train.pt |
| │ │ ├── image10001_NY_train.pt |
| │ │ └── image10002_NY_train.pt |
| │ │ ... (5000 files total) |
| │ │ ... (11 dirs total) |
| │ ├── NZ |
| │ │ ├── 0 |
| │ │ │ ├── image0_NZ_train.pt |
| │ │ │ ├── image1000_NZ_train.pt |
| │ │ │ └── image1001_NZ_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ ├── 5000 |
| │ │ │ ├── image5000_NZ_train.pt |
| │ │ │ ├── image5001_NZ_train.pt |
| │ │ │ └── image5002_NZ_train.pt |
| │ │ │ ... (5000 files total) |
| │ │ └── 10000 |
| │ │ ├── image10000_NZ_train.pt |
| │ │ ├── image10001_NZ_train.pt |
| │ │ └── image10002_NZ_train.pt |
| │ │ ... (5000 files total) |
| │ │ ... (11 dirs total) |
| │ ├── processed-flag-all |
| │ ├── processed-flag-CH |
| │ └── processed-flag-NY |
| │ ... (8 files total) |
| ├── val |
| │ ├── CH |
| │ │ └── 0 |
| │ │ ├── image0_CH_val.pt |
| │ │ ├── image100_CH_val.pt |
| │ │ └── image101_CH_val.pt |
| │ │ ... (529 files total) |
| │ ├── NY |
| │ │ └── 0 |
| │ │ ├── image0_NY_val.pt |
| │ │ ├── image100_NY_val.pt |
| │ │ └── image101_NY_val.pt |
| │ │ ... (529 files total) |
| │ ├── NZ |
| │ │ └── 0 |
| │ │ ├── image0_NZ_val.pt |
| │ │ ├── image100_NZ_val.pt |
| │ │ └── image101_NZ_val.pt |
| │ │ ... (529 files total) |
| │ ├── processed-flag-all |
| │ ├── processed-flag-CH |
| │ └── processed-flag-NY |
| │ ... (8 files total) |
| └── test |
| ├── CH |
| │ ├── 0 |
| │ │ ├── image0_CH_test.pt |
| │ │ ├── image1000_CH_test.pt |
| │ │ └── image1001_CH_test.pt |
| │ │ ... (5000 files total) |
| │ ├── 5000 |
| │ │ ├── image5000_CH_test.pt |
| │ │ ├── image5001_CH_test.pt |
| │ │ └── image5002_CH_test.pt |
| │ │ ... (5000 files total) |
| │ └── 10000 |
| │ ├── image10000_CH_test.pt |
| │ ├── image10001_CH_test.pt |
| │ └── image10002_CH_test.pt |
| │ ... (4400 files total) |
| ├── NY |
| │ ├── 0 |
| │ │ ├── image0_NY_test.pt |
| │ │ ├── image1000_NY_test.pt |
| │ │ └── image1001_NY_test.pt |
| │ │ ... (5000 files total) |
| │ ├── 5000 |
| │ │ ├── image5000_NY_test.pt |
| │ │ ├── image5001_NY_test.pt |
| │ │ └── image5002_NY_test.pt |
| │ │ ... (5000 files total) |
| │ └── 10000 |
| │ ├── image10000_NY_test.pt |
| │ ├── image10001_NY_test.pt |
| │ └── image10002_NY_test.pt |
| │ ... (4400 files total) |
| ├── NZ |
| │ ├── 0 |
| │ │ ├── image0_NZ_test.pt |
| │ │ ├── image1000_NZ_test.pt |
| │ │ └── image1001_NZ_test.pt |
| │ │ ... (5000 files total) |
| │ ├── 5000 |
| │ │ ├── image5000_NZ_test.pt |
| │ │ ├── image5001_NZ_test.pt |
| │ │ └── image5002_NZ_test.pt |
| │ │ ... (5000 files total) |
| │ └── 10000 |
| │ ├── image10000_NZ_test.pt |
| │ ├── image10001_NZ_test.pt |
| │ └── image10002_NZ_test.pt |
| │ ... (4400 files total) |
| ├── processed-flag-all |
| ├── processed-flag-CH |
| └── processed-flag-NY |
| ... (8 files total) |
| ``` |
|
|
| </details> |
|
|
| ## Pretrained model weights |
|
|
| ### Download |
|
|
| The recommended and fastest way to download the pretrained model weights is to run |
|
|
| ``` |
| python scripts/download_pretrained.py --model-root $MODEL_ROOT |
| ``` |
|
|
| Optionally you can also download the weights by running |
|
|
| ``` |
| git clone https://huggingface.co/rsi/PixelsPointsPolygons $MODEL_ROOT |
| ``` |
|
|
| Both options will download all checkpoints (as .pth) and results presented in the paper (as MS-COCO .json) into `$MODEL_ROOT` . |
|
|
| ## Code |
|
|
| ### Download |
|
|
| ``` |
| git clone https://github.com/raphaelsulzer/PixelsPointsPolygons |
| ``` |
|
|
| ### Installation |
|
|
| To create a conda environment named `p3` 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 | --> |
|
|
| ### Setup |
|
|
| The project supports hydra configuration which allows to modify any parameter either from a `.yaml` file or directly from the command line. |
|
|
| To setup the project structure we recommend to specify your `$DATA_ROOT` and `$MODEL_ROOT` in `config/host/default.yaml`. |
|
|
| To view all available configuration options run |
| ``` |
| python scripts/train.py --help |
| ``` |
|
|
|
|
|
|
| <!-- The most important parameters are described below: |
| <details> |
| <summary>CLI Parameters</summary> |
|
|
| ```text |
| ├── processed-flag-all |
| ├── processed-flag-CH |
| └── processed-flag-NY |
| ... (8 files total) |
| ``` |
|
|
| </details> --> |
|
|
| ### Predict demo tile |
|
|
| After downloading the model weights and setting up the code you can predict a demo tile by running |
|
|
| ``` |
| python scripts/predict_demo.py checkpoint=best_val_iou experiment=$MODEL_$MODALITY +image_file=demo_data/image0_CH_val.tif +lidar_file=demo_data/lidar0_CH_val.copc.laz |
| ``` |
| At least one of `image_file` or `lidar_file` has to be specified. `$MODEL` can be one of the following: `ffl`, `hisup` or `p2p`. `$MODALITY` can be `image`, `lidar` or `fusion`. |
| The result will be stored in `prediction.png`. |
|
|
|
|
| ### Reproduce paper results |
|
|
| To reproduce the results from the paper you can run the following commands |
|
|
| ``` |
| python scripts/modality_ablation.py |
| python scripts/lidar_density_ablation.py |
| python scripts/all_countries.py |
| ``` |
|
|
| ### Custom training, prediction and evaluation |
|
|
| We recommend to first setup a custom experiment file `$EXP_FILE` in `config/experiment/` following the structure of one of the existing files, e.g. `ffl_fusion.yaml`. You can then run |
|
|
| ``` |
| # train your model (on multiple GPUs) |
| torchrun --nproc_per_node=$NUM_GPU scripts/train.py experiment=$EXP_FILE |
| |
| # predict the test set with your model (on multiple GPUs) |
| torchrun --nproc_per_node=$NUM_GPU scripts/predict.py experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou |
| |
| # evaluate your prediction of the test set |
| python scripts/evaluate.py experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou |
| ``` |
|
|
| You could also continue training from a provided pretrained model with |
|
|
| ``` |
| # train your model (on a single GPU) |
| python scripts/train.py experiment=p2p_fusion checkpoint=latest |
| ``` |
|
|
| ## Citation |
|
|
| If you use our work please cite |
| ```bibtex |
| @misc{sulzer2025p3datasetpixelspoints, |
| title={The P$^3$ dataset: Pixels, Points and Polygons for Multimodal Building Vectorization}, |
| author={Raphael Sulzer and Liuyun Duan and Nicolas Girard and Florent Lafarge}, |
| year={2025}, |
| eprint={2505.15379}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2505.15379}, |
| } |
| ``` |
|
|
|
|
| ## 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) |
|
|
|
|
| ## License |
|
|
| The dataset is publish under the CC-BY-4.0 license. The code and pretrained models are published under an academic non-commerical license. See [LICENSE.md](LICENSE.md) for additional details. |
