3D object 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. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.
翻译:3D目标分离是计算机视觉中一个根本性和具有挑战性的问题,其应用在自主驱动、机器人、强化现实和医学图像分析方面,它得到了计算机视觉、图形和机器学习界的极大关注。传统上,3D分解是用手工制作的特征和设计的方法进行的,无法达到可接受的准确性,无法推广到大规模数据。由于在2D计算机视觉方面的巨大成功,深层次学习技术最近也成为三D分解任务的首选工具。这导致大量文献中的许多方法大量涌现,这些方法已经在不同的基准数据集上进行了评价。本文全面介绍了基于三D分解的深层次学习的最新进展,覆盖150多份文件。它总结了最常用的管道,讨论了其亮点和缺点,并分析了这些分解方法的竞争结果。根据分析,它也为未来提供了有希望的研究方向。