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In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments aredoi:10.1109/cvpr.2016.160 dblp:conf/cvpr/BibiZG16 fatcat:mvowpqrv25echihhapanl7qtoy