A point cloud is a collection of points, which is measured by time-of-flight information from LiDAR sensors, forming geometrical representations of the surrounding environment. With the algorithmic success of deep learning networks, point clouds are not only used in traditional application domains like localization or HD map construction but also in a variety of avenues including object classification, 3D object detection, or semantic segmentation. While point cloud analytics are gaining significant traction in both academia and industry, the computer architecture community has only recently begun exploring this important problem space. In this paper, we conduct a detailed, end-to-end characterization on deep learning based point cloud analytics workload, root-causing the frontend data preparation stage as a significant performance limiter. Through our findings, we discuss possible future directions to motivate continued research in this emerging application domain.

中文翻译：

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3D 点云分析深度学习的表征和分析


点云是点的集合，通过激光雷达传感器的飞行时间信息进行测量，形成周围环境的几何表示。随着深度学习网络算法的成功，点云不仅用于定位或高清地图构建等传统应用领域，而且还用于包括对象分类、3D 对象检测或语义分割等多种途径。虽然点云分析在学术界和工业界都获得了巨大的关注，但计算机架构社区最近才开始探索这个重要的问题空间。在本文中，我们对基于深度学习的点云分析工作负载进行了详细的端到端表征，从根本上导致前端数据准备阶段成为重要的性能限制因素。通过我们的发现，我们讨论了未来可能的方向，以推动这一新兴应用领域的持续研究。