  Epipolar constraints are at the core of feature matching and depth estimation
in current multi-person multi-camera 3D human pose estimation methods. Despite
the satisfactory performance of this formulation in sparser crowd scenes, its
effectiveness is frequently challenged under denser crowd circumstances mainly
due to two sources of ambiguity. The first is the mismatch of human joints
resulting from the simple cues provided by the Euclidean distances between
joints and epipolar lines. The second is the lack of robustness from the naive
formulation of the problem as a least squares minimization. In this paper, we
depart from the multi-person 3D pose estimation formulation, and instead
reformulate it as crowd pose estimation. Our method consists of two key
components: a graph model for fast cross-view matching, and a maximum a
posteriori (MAP) estimator for the reconstruction of the 3D human poses. We
demonstrate the effectiveness and superiority of our proposed method on four
benchmark datasets.
