Point cloud is point sets defined in 3D metric space. Point cloud has become one of the most significant data format for 3D representation. Its gaining increased popularity as a result of increased availability of acquisition devices, such as LiDAR, as well as increased application in areas such as robotics, autonomous driving, augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision, becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes use of deep learning for its processing directly very challenging. Earlier approaches overcome this challenge by preprocessing the point cloud into a structured grid format at the cost of increased computational cost or lost of depth information. Recently, however, many state-of-the-arts deep learning techniques that directly operate on point cloud are being developed. This paper contains a survey of the recent state-of-the-art deep learning techniques that mainly focused on point cloud data. We first briefly discussed the major challenges faced when using deep learning directly on point cloud, we also briefly discussed earlier approaches which overcome the challenges by preprocessing the point cloud into a structured grid. We then give the review of the various state-of-the-art deep learning approaches that directly process point cloud in its unstructured form. We introduced the popular 3D point cloud benchmark datasets. And we also further discussed the application of deep learning in popular 3D vision tasks including classification, segmentation and detection.
翻译:点云是3D 度空间定义的点云。 点云已成为3D 代表中最重要的数据格式之一。 点云由于获得设备(如LIDAR)的可用性增加,以及在机器人、自主驱动、增强和虚拟现实等领域的应用增加,越来越受欢迎。 深度学习现在是计算机视野中数据处理的最有力工具,成为分类、 分解和探测等任务最受欢迎的技术。 虽然深层学习技术主要应用于结构化网格的数据中, 点云的深度探测技术是非结构化的。 点云的不结构化使得利用深层次的视野处理具有直接的难度。 早期的方法克服了这一挑战, 将点云云预处理成结构化的电网格式, 成本增加计算成本或深度信息损失。 然而,最近正在开发许多直接在点云层上运行的尖端深层次学习技术。 本文载有对最近以点云层数据为主的高级深度探测技术的调查。 我们第一次简要讨论了在使用深度的云层分析过程中所面临的主要挑战。 我们还通过直接学习云层分析, 直接学习了各种云层分析过程。