The P 3 Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization Raphael Sulzer 1,2 Liuyun Duan 1 Nicolas Girard 1 Florent Lafarge 2 1 LuxCarta Technology 2 Centre Inria d'Université Côte d'Azur Figure 1 : A view of our dataset of Zurich, Switzerland Table of Contents Abstract Highlights Dataset Pretrained model weights Code Citation Acknowledgements Abstract We present the P 3 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 quali…
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