  3D human pose estimation aims to reconstruct the human skeleton of all the
individuals in a scene by detecting several body joints. The creation of
accurate and efficient methods is required for several real-world applications
including animation, human-robot interaction, surveillance systems or sports,
among many others. However, several obstacles such as occlusions, random camera
perspectives, or the scarcity of 3D labelled data, have been hampering the
models' performance and limiting their deployment in real-world scenarios. The
higher availability of cameras has led researchers to explore multi-view
solutions due to the advantage of being able to exploit different perspectives
to reconstruct the pose.
  Thus, the goal of this survey is to present an overview of the methodologies
used to estimate the 3D pose in multi-view settings, understand what were the
strategies found to address the various challenges and also, identify their
limitations. Based on the reviewed articles, it was possible to find that no
method is yet capable of solving all the challenges associated with the
reconstruction of the 3D pose. Due to the existing trade-off between complexity
and performance, the best method depends on the application scenario.
Therefore, further research is still required to develop an approach capable of
quickly inferring a highly accurate 3D pose with bearable computation cost. To
this goal, techniques such as active learning, methods that learn with a low
level of supervision, the incorporation of temporal consistency, view
selection, estimation of depth information and multi-modal approaches might be
interesting strategies to keep in mind when developing a new methodology to
solve this task.
