P$^3$数据集：用于多模态建筑物矢量化的像素、点和多边形
The P$^3$ Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization
AI总结：
介绍用于建筑物矢量化的多模态P$^3$数据集，由多源数据构建，含大量高精度激光雷达点等。证明激光雷达点云可用于预测建筑物多边形，融合激光雷达和图像能提高预测精度与几何质量，数据集公开。
摘要：
我们展示了P$^3$数据集，这是一个用于建筑物矢量化的大规模多模态基准，由跨三大洲收集的航空激光雷达点云、高分辨率航空图像和矢量化二维建筑物轮廓构建而成。该数据集包含超过100亿个具有分米级精度的激光雷达点和地面采样距离为25厘米的RGB图像。虽然许多现有数据集主要关注图像模态，但P$^3$通过纳入密集的3D信息提供了一个补充视角。我们证明，在混合和端到端学习框架中，激光雷达点云都是预测建筑物多边形的强大模态。此外，融合航空激光雷达和图像进一步提高了预测多边形的准确性和几何质量。P$^3$数据集以及用于建筑物多边形预测的三个最先进模型的代码和预训练权重可在此https URL上公开获取。
英文摘要：
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 centimeter. 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$^3$ 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 .
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