2020-09-01
3D point cloud classification is an important task with applications in robotics, augmented reality and urban planning. Recent advances in Machine Learning and Computer Vision have proven that complex real-world tasks require large training data sets for classifier training.
3D点云分类对机器人、增强现实和城市规划等应用具有重要的作用，机器学习和计算机视觉领域的最新研究进展证明了针对现实世界的点云分类需要大量的训练数据对分类器进行训练。
At the same time, until now there were no data sets for 3D point cloud classification which would be sufficiently rich in both object representations and number of labelled points. For example, the well-known Oakland data set contains less than 2 million labelled points. Another popular data set, the NYU benchmark, provides only indoor scenes. Finally, both Sydney Urban Objects data set and the IQmulus & TerraMobilita Contest use a 3D Velodyne LIDAR mounted on a car which provides much lower point density than a static scanner. The same counts for the Vaihingen3D airborne benchmark.
同时，直到目前为止，仍然没有公开的三维点云数据集可以同时满足目标表示和带标架的样本点。例如，比较有名的Oakland数据集包含了不超过200万个带标记的点。另一个比较流行的NYU benchmark数据集只提供了室内场景的数据。最后，Sydney Urban Object和IQmulus & TerraMobilita Contest 利用安装在车上的LIDAR扫描仪采集的数据密度远低于静止状态下的扫描仪，而Vaihingen3D airborne benchmark数据集也是一样的。
This benchmark closes the gap and provides a large labelled 3D point cloud data set of natural scenes with over 4 billion points in total. It also covers a range of diverse urban scenes: churches, streets, railroad tracks, squares, villages, soccer fields, castles to name just a few. The point clouds we provide are scanned statically with state-of-the-art equipment and contain very fine details. Our goal is to help data-demanding methods like deep neural nets to unleash their full power and to learn richer 3D representations than it was ever possible before.
本数据集克服了上述的问题，提供了超过40亿个自然场景下的三维点云数据，它包含了城市场景下的诸多要素：教堂、街道、铁轨、广场、村庄、足球场、少部分有名字的城堡等。这些点云数据都是采用当前最先进的设备在静止状态下获取的，包含了非常精细的细节信息。我们的目的是帮助如深度神经网络等数据驱动的方法来发挥他们的作用，学习到更加丰富的3D表达。