  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.
