查看文章

Undoubtedly, the object is the primary factor in 3D single-object tracking (SOT) tasks. However, prior Siamese-based trackers overlook the adverse effects resulting from randomly dropped object points during backbone sampling, hindering the prediction of accurate bounding boxes (BBoxes). Therefore, developing an approach that maximizes the preservation of object points and their object-aware features is of the utmost significance. To address this, we propose an object-preserving Siamese network ( OPSNet ) that can effectively maintain object integrity and boost tracking performance. First, an object highlighting module amplifies the object-aware features and extracts discriminative features from the template and search area. Next, object-preserving sampling selects object candidates, obtains object-preserving search area seeds, and discards background points that have less impact on tracking. Finally, an object …