3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely labor intensive and requires professional skills. The performance limitation caused by insufficient datasets is called data hunger problem. This research provides a comprehensive survey and experimental study on the question: are we hungry for 3D LiDAR data for semantic segmentation? The studies are conducted at three levels. First, a broad review to the main 3D LiDAR datasets is conducted, followed by a statistical analysis on three representative datasets to gain an in-depth view on the datasets' size and diversity, which are the critical factors in learning deep models. Second, a systematic review to the state-of-the-art 3D semantic segmentation is conducted, followed by experiments and cross examinations of three representative deep learning methods to find out how the size and diversity of the datasets affect deep models' performance. Finally, a systematic survey to the existing efforts to solve the data hunger problem is conducted on both methodological and dataset's viewpoints, followed by an insightful discussion of remaining problems and open questions To the best of our knowledge, this is the first work to analyze the data hunger problem for 3D semantic segmentation using deep learning techniques that are addressed in the literature review, statistical analysis, and cross-dataset and cross-algorithm experiments. We share findings and discussions, which may lead to potential topics in future works.
翻译:3D 语义分解是机器人和自主驱动应用程序的一项根本任务。 最近的工作侧重于使用深层学习技术,而开发3D LiDAR 精细附加说明的3D LiDAR 数据集则耗费大量人力,需要专业技能。 数据集不足导致的性能限制被称为数据饥饿问题。 这项研究提供了一个全面调查和实验性研究,探讨以下问题: 我们是否渴望3D LiDAR 的语义分解数据? 研究在三个层次上进行。 首先,对主要的 3D LIDAR 跨层数据集进行广泛的审查,然后对三个有代表性的数据集进行统计分析,以深入了解数据集的规模和多样性,然后,对三个有代表性的3D 语义分解系统进行业绩限制。 最后,对三个有代表性的数据集进行统计分析,在方法和数据集的大小和多样性方面进行深入分析,然后,对当前解决饥饿问题的努力进行系统化调查,在方法和数据集解的分层分析中,然后,对数据分层分析数据分解问题进行分析。