  The 3D point cloud (3DPC) has significantly evolved and benefited from the
advance of deep learning (DL). However, the latter faces various issues,
including the lack of data or annotated data, the existence of a significant
gap between training data and test data, and the requirement for high
computational resources. To that end, deep transfer learning (DTL), which
decreases dependency and costs by utilizing knowledge gained from a source
data/task in training a target data/task, has been widely investigated.
Numerous DTL frameworks have been suggested for aligning point clouds obtained
from several scans of the same scene. Additionally, DA, which is a subset of
DTL, has been modified to enhance the point cloud data's quality by dealing
with noise and missing points. Ultimately, fine-tuning and DA approaches have
demonstrated their effectiveness in addressing the distinct difficulties
inherent in point cloud data. This paper presents the first review shedding
light on this aspect. it provides a comprehensive overview of the latest
techniques for understanding 3DPC using DTL and domain adaptation (DA).
Accordingly, DTL's background is first presented along with the datasets and
evaluation metrics. A well-defined taxonomy is introduced, and detailed
comparisons are presented, considering different aspects such as different
knowledge transfer strategies, and performance. The paper covers various
applications, such as 3DPC object detection, semantic labeling, segmentation,
classification, registration, downsampling/upsampling, and denoising.
Furthermore, the article discusses the advantages and limitations of the
presented frameworks, identifies open challenges, and suggests potential
research directions.
