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Cross-view person matching and 3D human pose estimation in multi-cameranetworks are particularly difficult when the cameras are extrinsicallyuncalibrated. Existing efforts generally require large amounts of 3D data fortraining neural networks or known camera poses for geometric constraints tosolve the problem. However, camera poses and 3D data annotation are usuallyexpensive and not always available. We present a method, PME, that solves thetwo tasks without requiring either information. Our idea is to addresscross-view person matching as a clustering problem using each person as acluster center, then obtain correspondences from person matches, and estimate3D human poses through multi-view triangulation and bundle adjustment. We solvethe clustering problem by introducing a "size constraint" using the number ofcameras and a "source constraint" using the fact that two people from the samecamera view should not match, to narrow the solution space to a small feasibleregion. The 2D human poses used in clustering are obtained through apre-trained 2D pose detector, so our method does not require expensive 3Dtraining data for each new scene. We extensively evaluate our method on threeopen datasets and two indoor and outdoor datasets collected using arbitrarilyset cameras. Our method outperforms other methods by a large margin oncross-view person matching, reaches SOTA performance on 3D human poseestimation without using either camera poses or 3D training data, and showsgood generalization ability across five datasets of various environmentsettings.

Multi-View Person Matching and 3D Pose Estimation with Arbitrary Uncalibrated Camera Networks