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.
翻译:在基于3D的景点云的学习已得到广泛关注,因为它在许多领域的应用很有希望,而且有良好的附加说明和多源数据集可以推动开发这些数据驱动的方法。为了便利对这一领域的研究,我们为多个户外场景理解任务提供了一套内容丰富的3D点云数据集,并为其分层任务提供了一个有效的学习框架。数据集是通过新加坡国立大学校园无人驾驶航空飞行器图像的摄影测量处理生成的,并且以等级标签和实例标签为标志。在此基础上,我们为3D点云分层设计了一个等级学习问题,并提出了评估不同等级结构一致性的衡量标准。为解决这一问题,我们提议了一种两阶段方法,包括多任务(MT)学习和等级共振(HE),并进行了一致考虑。实验结果表明拟议方法和我们等级说明的潜在优势。此外,我们用等级标签和实例分层标签对标准分解结果进行了点评。此外,我们根据它为3D点云分层分层和实例分解结果设定了一个等级问题等级,可在https://3ddddddataset所有数据源码上查阅。