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We tackle a new problem of multi-view camera and subject registration in thebird's eye view (BEV) without pre-given camera calibration. This is a verychallenging problem since its only input is several RGB images from differentfirst-person views (FPVs) for a multi-person scene, without the BEV image andthe calibration of the FPVs, while the output is a unified plane with thelocalization and orientation of both the subjects and cameras in a BEV. Wepropose an end-to-end framework solving this problem, whose main idea can bedivided into following parts: i) creating a view-transform subject detectionmodule to transform the FPV to a virtual BEV including localization andorientation of each pedestrian, ii) deriving a geometric transformation basedmethod to estimate camera localization and view direction, i.e., the cameraregistration in a unified BEV, iii) making use of spatial and appearanceinformation to aggregate the subjects into the unified BEV. We collect a newlarge-scale synthetic dataset with rich annotations for evaluation. Theexperimental results show the remarkable effectiveness of our proposed method.

From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration