  Learning on 3D scene-based point cloud has received extensive attention as
its promising application in many fields, and well-annotated and multisource
datasets can catalyze the development of those data-driven approaches. To
facilitate the research of this area, we present a richly-annotated 3D point
cloud dataset for multiple outdoor scene understanding tasks and also an
effective learning framework for its hierarchical segmentation task. The
dataset was generated via the photogrammetric processing on unmanned aerial
vehicle (UAV) images of the National University of Singapore (NUS) campus, and
has been point-wisely annotated with both hierarchical and instance-based
labels. Based on it, we formulate a hierarchical learning problem for 3D point
cloud segmentation and propose a measurement evaluating consistency across
various hierarchies. To solve this problem, a two-stage method including
multi-task (MT) learning and hierarchical ensemble (HE) with consistency
consideration is proposed. Experimental results demonstrate the superiority of
the proposed method and potential advantages of our hierarchical annotations.
In addition, we benchmark results of semantic and instance segmentation, which
is accessible online at https://3d.dataset.site with the dataset and all source
codes.
