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README.md

@@ -1,9 +1,27 @@
+---
+license: cc-by-4.0
+pretty_name: Pixels Point Polygons
+size_categories:
+- 10B<n<100B
+task_categories:
+- image-segmentation
+tags:
+- IGN
+- Aerial
+- Satellite
+- Environement
+- Multimodal
+- Earth Observation
+---
+
+
 <div align="center">
-    <h2 align="center">Pixels, Points, Polygons: A Global Dataset and Baseline for Multimodal Building Vectorization</h2>
+    <h2 align="center">The P<sup>3</sup> dataset: Pixels, Points and Polygons <br> for Multimodal Building Vectorization</h2>
     <!-- <h3 align="center">Arxiv</h3> -->
     <!-- <h3 align="center"><a href="https://raphaelsulzer.de/">Raphael Sulzer<sup>1,2</sup></a><br></h3> -->
-    <h3><align="center">Raphael Sulzer<sup>1,2</sup></a></h3>
-    <align="center"><sup>1</sup>LuxCarta   <sup>2</sup>Inria
+    <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="./media/teaser.jpg" width=100% height=100%>
     <b>Figure 1</b>: A view of our dataset of Zurich, Switzerland
 </div>
@@ -13,7 +31,7 @@
 
 ## Abstract:
 
-asd
+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$^3$ 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.
 
 ## Highlights