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Learning on 3D scene-based point cloud has received extensive attention asits promising application in many fields, and well-annotated and multisourcedatasets can catalyze the development of those data-driven approaches. Tofacilitate the research of this area, we present a richly-annotated 3D pointcloud dataset for multiple outdoor scene understanding tasks and also aneffective learning framework for its hierarchical segmentation task. Thedataset was generated via the photogrammetric processing on unmanned aerialvehicle (UAV) images of the National University of Singapore (NUS) campus, andhas been point-wisely annotated with both hierarchical and instance-basedlabels. Based on it, we formulate a hierarchical learning problem for 3D pointcloud segmentation and propose a measurement evaluating consistency acrossvarious hierarchies. To solve this problem, a two-stage method includingmulti-task (MT) learning and hierarchical ensemble (HE) with consistencyconsideration is proposed. Experimental results demonstrate the superiority ofthe proposed method and potential advantages of our hierarchical annotations.In addition, we benchmark results of semantic and instance segmentation, whichis accessible online at https://3d.dataset.site with the dataset and all sourcecodes.

Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene