  The process of segmenting point cloud data into several homogeneous areas
with points in the same region having the same attributes is known as 3D
segmentation. Segmentation is challenging with point cloud data due to
substantial redundancy, fluctuating sample density and lack of apparent
organization. The research area has a wide range of robotics applications,
including intelligent vehicles, autonomous mapping and navigation. A number of
researchers have introduced various methodologies and algorithms. Deep learning
has been successfully used to a spectrum of 2D vision domains as a prevailing
A.I. methods. However, due to the specific problems of processing point clouds
with deep neural networks, deep learning on point clouds is still in its
initial stages. This study examines many strategies that have been presented to
3D instance and semantic segmentation and gives a complete assessment of
current developments in deep learning-based 3D segmentation. In these
approaches benefits, draw backs, and design mechanisms are studied and
addressed. This study evaluates the impact of various segmentation algorithms
on competitiveness on various publicly accessible datasets, as well as the most
often used pipelines, their advantages and limits, insightful findings and
intriguing future research directions.
