The P³ Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization

Abstract. We present P^3, a large-scale multimodal dataset for building vectorization, including aerial LiDAR point clouds, aerial images, and vectorized 2D building outlines, collected across three continents. P³ contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 centimeters. While many existing datasets focus on the image modality, P³ offers a complementary perspective by 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 LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P³ 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.