3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. 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 many 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 a recent in-depth survey that covers all 3D data modalities and application domains. This paper fills the gap and comprehensively surveys the recent progress in deep learning-based 3D segmentation techniques. We cover over 230 works from the last six years, analyze their strengths and limitations, and discuss 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.

中文翻译：

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计算机视觉中基于深度学习的 3D 分割：一项调查


3D 分割是计算机视觉中一个基本且具有挑战性的问题，在自动驾驶和机器人技术中的应用也是如此。它受到了计算机视觉、图形和机器学习社区的极大关注。基于手工制作特征和机器学习分类器的传统 3D 分割方法缺乏泛化能力。在 2D 计算机视觉成功的推动下，深度学习技术最近已成为 3D 分割任务的首选工具。这导致文献中涌入许多方法，这些方法已在不同的基准数据集上进行了评估。虽然存在关于 RGB-D 和点云分割的调查论文，但最近缺乏涵盖所有 3D 数据模态和应用领域的深入调查。本文填补了这一空白，并全面调查了基于深度学习的 3D 分割技术的最新进展。我们涵盖了过去六年的 230 多项作品，分析了它们的优势和局限性，并讨论了它们在基准数据集上的竞争结果。该调查总结了最常用的管道，最后强调了未来有前景的研究方向。