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Siamese network has been a de facto benchmark framework for 3D LiDAR objecttracking with a shared-parametric encoder extracting features from template andsearch region, respectively. This paradigm relies heavily on an additionalmatching network to model the cross-correlation/similarity of the template andsearch region. In this paper, we forsake the conventional Siamese paradigm andpropose a novel single-branch framework, SyncTrack, synchronizing the featureextracting and matching to avoid forwarding encoder twice for template andsearch region as well as introducing extra parameters of matching network. Thesynchronization mechanism is based on the dynamic affinity of the Transformer,and an in-depth analysis of the relevance is provided theoretically. Moreover,based on the synchronization, we introduce a novel Attentive Points-Samplingstrategy into the Transformer layers (APST), replacing the random/FarthestPoints Sampling (FPS) method with sampling under the supervision of attentiverelations between the template and search region. It implies connectingpoint-wise sampling with the feature learning, beneficial to aggregating moredistinctive and geometric features for tracking with sparse points. Extensiveexperiments on two benchmark datasets (KITTI and NuScenes) show that SyncTrackachieves state-of-the-art performance in real-time tracking.

Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking