Siamese network has been a de facto benchmark framework for 3D LiDAR object
tracking with a shared-parametric encoder extracting features from template and
search region, respectively. This paradigm relies heavily on an additional
matching network to model the cross-correlation/similarity of the template and
search region. In this paper, we forsake the conventional Siamese paradigm and
propose a novel single-branch framework, SyncTrack, synchronizing the feature
extracting and matching to avoid forwarding encoder twice for template and
search region as well as introducing extra parameters of matching network. The
synchronization 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-Sampling
strategy into the Transformer layers (APST), replacing the random/Farthest
Points Sampling (FPS) method with sampling under the supervision of attentive
relations between the template and search region. It implies connecting
point-wise sampling with the feature learning, beneficial to aggregating more
distinctive and geometric features for tracking with sparse points. Extensive
experiments on two benchmark datasets (KITTI and NuScenes) show that SyncTrack
achieves state-of-the-art performance in real-time tracking.

本文提出了一种新颖的单分支框架 SyncTrack，通过同步特征提取和匹配，避免了传统的对模板和搜索区域分别进行编码器传递以及引入额外的匹配网络参数，并基于 Transformer 的动态关联实现了同步机制。此外，引入了一种新颖的注意力点采样策略 (APST) 来代替随机 / Farthest Points Sampling (FPS) 方法，通过注意力关系来指导采样，将点采样与特征学习相连接，有助于聚合更加明显和几何特征以进行稀疏点跟踪。在两个基准数据集（KITTI 和 NuScenes）上进行的广泛实验表明，SyncTrack 在实时跟踪方面取得了最先进的性能。