3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Conventional methods for 3D segmentation, based on hand-crafted features and machine learning classifiers, lack generalization ability. Driven by their success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. Whereas survey papers on RGB-D and point cloud segmentation exist, there is a lack of an in-depth and recent survey that covers all 3D data modalities and application domains. This paper fills the gap and provides a comprehensive survey of the recent progress made in deep learning based 3D segmentation. It covers over 180 works, analyzes their strengths and limitations and discusses their competitive results on benchmark datasets. The survey provides a summary of the most commonly used pipelines and finally highlights promising research directions for the future.

翻译：3D分割是计算机视觉中一个基础且具有挑战性的问题，在自动驾驶、机器人、增强现实和医学图像分析等领域具有广泛应用。该问题已引起计算机视觉、图形学和机器学习领域的广泛关注。传统基于手工特征和机器学习分类器的3D分割方法缺乏泛化能力。受深度学习在2D计算机视觉领域成功应用的驱动，该技术近期已成为3D分割任务的首选工具。这导致文献中涌现大量在不同基准数据集上评估的方法。虽然已存在针对RGB-D和点云分割的综述论文，但尚缺乏一篇涵盖所有3D数据模态和应用领域的深入且前沿的综述。本文填补了这一空白，对基于深度学习的3D分割领域最新进展进行全面综述。论文涵盖180余项工作，分析它们的优势与局限，并讨论其在基准数据集上的竞争性结果。本综述总结了最常用的技术流程，最后指出了未来有价值的研究方向。