论文标题
polylidar3d-从3D数据中提取快速多边形
Polylidar3D -- Fast Polygon Extraction from 3D Data
论文作者
论文摘要
由3D点云捕获的平面通常用于定位，映射和建模。密集的点云处理具有较高的计算和内存成本，因此需要低维的平坦表面（例如多边形）。我们提出PolylIdar3D，一种非凸多边形提取算法，将其作为输入无组织的3D点云（例如LIDAR数据），有组织的点云（例如范围图像）或用户提供的网格。非凸多边形代表具有内部切口的环境中的平坦表面，代表障碍物或孔。 Polylidar3D前端将输入数据转换为半边的三角形网格。该表示形式为后续后端处理提供了常见的输入数据抽象。 Polylidar3D后端由四种核心算法组成：网状平滑，显性平面正常估计，平面段提取，最后是多边形提取。 polylidar3d显示出非常快的速度，可在可用时使用CPU多线程和GPU加速度。我们使用现实世界中的数据集演示了Polylidar3D的多功能性和速度，包括用于屋顶映射的空中激光雷德点云，用于道路表面检测的自动驾驶激光雷达点云以及用于室内地板/墙壁检测的RGBD摄像机。我们还在具有挑战性的平面分割基准数据集上评估了Polylidar3D。结果始终显示出极好的速度和准确性。
Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of input data abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D's versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.
