Anonymous for ICCV 2019 Review. "NUS3D: a Large-scale 3D Point Cloud Dataset for Outdoor Scene Understanding." Working paper, under review at ICCV 2019. 2019.
This dataset was generated from unmanned aerial vehicle photography and covers 1.58 square km area of National University of Singapore campus. It consists of around 1 billion points.
Each point is annotated by a hierarchical and instance-based label. There are 2,530 modality-based instances, 24 semantic classes and 6 pattern-based regions.
This dataset can be used for object detection, semantic segmentation, instance segmentation, fast scene understanding, object detection, 3D model reconstruction and etc.
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