OpenGF：基于全球开放ALS点云的超大规模地面滤波数据集
OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World
摘要：
由于地形的剧烈变化和地物结构的复杂性，地面滤波一直是高精度数字高程模型自动生成中的一个研究热点，但一直未能完全解决。监督式深度学习算法在3D场景理解方面的最新突破为更好地解决此类问题带来了新的解决方案。然而，专门用于地面提取的大规模和场景丰富的公共数据集很少，这在很大程度上限制了有效的基于深度学习的地面滤波方法的发展。为此，我们提出了OpenGF，第一个超大规模地面过滤数据集，覆盖超过47个$km^2$的9个不同的典型地形场景建立在开放的ALS点云的4个不同的国家在世界各地。OpenGF包含超过5亿个精细标记的地面和非地面点，是事实标准ISPRS filtertest数据集标记点数量的数千倍。我们在我们的数据集上广泛评估了最先进的基于规则的算法和3D语义分割网络的性能，并提供了全面的分析。结果证实了OpenGF能够有效地训练深度学习模型。该数据集在https://github.com/Nathan-UW/OpenGF上发布，以促进地面滤波和大规模3D地理环境理解的更先进研究。
英文摘要：
Ground filtering has remained a widely studied but incompletely resolved bottleneck for decades in the automatic generation of high-precision digital elevation model, due to the dramatic changes of topography and the complex structures of objects. The recent breakthrough of supervised deep learning algorithms in 3D scene understanding brings new solutions for better solving such problems. However, there are few large-scale and scene-rich public datasets dedicated to ground extraction, which considerably limits the development of effective deep-learning-based ground filtering methods. To this end, we present OpenGF, first Ultra-Large-Scale Ground Filtering dataset covering over 47 $km^2$ of 9 different typical terrain scenes built upon open ALS point clouds of 4 different countries around the world. OpenGF contains more than half a billion finely labeled ground and non-ground points, thousands of times the number of labeled points than the de facto standard ISPRS filtertest dataset. We extensively evaluate the performance of state-of-the-art rule-based algorithms and 3D semantic segmentation networks on our dataset and provide a comprehensive analysis. The results have confirmed the capability of OpenGF to train deep learning models effectively. This dataset is released at https://github.com/Nathan-UW/OpenGF to promote more advancing research for ground filtering and large-scale 3D geographic environment understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
