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Existing approaches for multi-view multi-person 3D pose estimation explicitlyestablish cross-view correspondences to group 2D pose detections from multiplecamera views and solve for the 3D pose estimation for each person. Establishingcross-view correspondences is challenging in multi-person scenes, and incorrectcorrespondences will lead to sub-optimal performance for the multi-stagepipeline. In this work, we present our multi-view 3D pose estimation approachbased on plane sweep stereo to jointly address the cross-view fusion and 3Dpose reconstruction in a single shot. Specifically, we propose to perform depthregression for each joint of each 2D pose in a target camera view. Cross-viewconsistency constraints are implicitly enforced by multiple reference cameraviews via the plane sweep algorithm to facilitate accurate depth regression. Weadopt a coarse-to-fine scheme to first regress the person-level depth followedby a per-person joint-level relative depth estimation. 3D poses are obtainedfrom a simple back-projection given the estimated depths. We evaluate ourapproach on benchmark datasets where it outperforms previous state-of-the-artswhile being remarkably efficient. Our code is available athttps://github.com/jiahaoLjh/PlaneSweepPose.

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo