  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.
