  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.
