  This paper addresses the problem of 3D pose estimation for multiple people in
a few calibrated camera views. The main challenge of this problem is to find
the cross-view correspondences among noisy and incomplete 2D pose predictions.
Most previous methods address this challenge by directly reasoning in 3D using
a pictorial structure model, which is inefficient due to the huge state space.
We propose a fast and robust approach to solve this problem. Our key idea is to
use a multi-way matching algorithm to cluster the detected 2D poses in all
views. Each resulting cluster encodes 2D poses of the same person across
different views and consistent correspondences across the keypoints, from which
the 3D pose of each person can be effectively inferred. The proposed convex
optimization based multi-way matching algorithm is efficient and robust against
missing and false detections, without knowing the number of people in the
scene. Moreover, we propose to combine geometric and appearance cues for
cross-view matching. The proposed approach achieves significant performance
gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the
Campus and Shelf datasets, respectively), while being efficient for real-time
applications.
