With the rapid advancement of 3D sensors, there is an increasing demand for 3D scene understanding and an increasing number of 3D deep learning algorithms have been proposed. However, a large-scale and richly annotated 3D point cloud dataset is critical to understanding complicated road and urban scenes. Motivated by the need to bridge the gap between the rising demand for 3D urban scene understanding and limited LiDAR point cloud datasets, this paper proposes a richly annotated WHU-Urban3D dataset and an effective method for semantic instance segmentation. WHU-Urban3D stands out from existing datasets due to its distinctive features: (1) extensive coverage of both Airborne Laser Scanning and Mobile Laser Scanning point clouds, along with panoramic images; (2) containing large-scale road and urban scenes in different cities (over area), with richly point-wise semantic instance labels (over 200 million points); (3) inclusion of particular attributes (e.g., reflected intensity, number of returns) in addition to 3D coordinates. This paper also provides the performance of several representative baseline methods and outlines potential future works and challenges for fully exploiting this dataset. The WHU-Urban3D dataset is publicly accessible at .

中文翻译：

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WHU-Urban3D：用于语义实例分割的城市场景 LiDAR 点云数据集

随着3D传感器的快速发展，对3D场景理解的需求不断增加，越来越多的3D深度学习算法被提出。然而，大规模且注释丰富的 3D 点云数据集对于理解复杂的道路和城市场景至关重要。为了弥合不断增长的 3D 城市场景理解需求与有限的 LiDAR 点云数据集之间的差距，本文提出了一个带有丰富注释的 WHU-Urban3D 数据集和一种有效的语义实例分割方法。WHU-Urban3D 因其独特的功能而在现有数据集中脱颖而出：（1）广泛覆盖机载激光扫描和移动激光扫描点云以及全景图像；（2）包含不同城市（跨区域）的大规模道路和城市场景，具有丰富的逐点语义实例标签（超过2亿个点）；(3) 除了 3D 坐标之外，还包含特定属性（例如，反射强度、返回次数）。本文还提供了几种代表性基线方法的性能，并概述了充分利用该数据集的潜在未来工作和挑战。WHU-Urban3D 数据集可在 上公开访问。