  Obviously, the object is the key factor of the 3D single object tracking
(SOT) task. However, previous Siamese-based trackers overlook the negative
effects brought by randomly dropped object points during backbone sampling,
which hinder trackers to predict accurate bounding boxes (BBoxes). Exploring an
approach that seeks to maximize the preservation of object points and their
object-aware features is of particular significance. Motivated by this, we
propose an Object Preserving Siamese Network
  (OPSNet), which can significantly maintain object integrity and boost
tracking performance. Firstly, the object highlighting module enhances the
object-aware features and extracts discriminative features from template and
search area. Then, the object-preserved sampling selects object candidates to
obtain object-preserved search area seeds and drop the background points that
contribute less to tracking. Finally, the object localization network precisely
locates 3D BBoxes based on the object-preserved search area seeds. Extensive
experiments demonstrate our method outperforms the state-of-the-art performance
(9.4% and 2.5% success gain on KITTI and Waymo Open Dataset respectively).
