Point cloud-based 3D single object tracking (3D SOT) plays a pivotal role in applications such as autonomous driving and robotic vision. Despite recent progress, most existing approaches rely solely on current-frame features for target localization. This approach overlooks temporal information that is crucial for robust tracking under occlusion, appearance variations, and sparse point clouds. In addition, the effectiveness of 3D SOT largely depends on the quality of feature fusion between the target template and the search region. Traditional fusion strategies often suffer from limited interaction capacity and weak discriminative representation. To address these challenges, we propose DT-Tracker, which performs multi-layer bidirectional feature interaction and temporal cue propagation to improve tracking robustness and feature discrimination capability. Specifically, we introduce a Dual-Context Propagation Network that applies bidirectional cross-attention across multiple layers between the template and search region, enabling deep semantic alignment and progressive feature refinement. Furthermore, we design a Temporal Context Fusion module that adaptively incorporates temporal cues from historical fusion features into the current frame, effectively improving resilience to occlusion and appearance drift. Extensive experiments on the KITTI and nuScenes datasets demonstrate that DT-Tracker achieves competitive results compared to existing representative methods.

Graph alignment aims to find correspondent nodes between two graphs. Most existing algorithms assume that correspondent nodes in different graphs have similar local structures. However, this principle may not apply to some real-world application scenarios when two graphs have different densities. Some correspondent node pairs may have very different local structures in these cases. Nevertheless, correspondent nodes are expected to have similar importance, inspiring us to exploit global topology consistency for graph alignment. This paper presents GTCAlign, an unsupervised graph alignment framework based on global topology consistency. An indicating matrix is calculated to show node pairs with consistent global topology based on a comprehensive centrality metric. A graph convolutional network (GCN) encodes local structural and attributive information into low-dimensional node embeddings. Then, node similarities are computed based on the obtained node embeddings under the guidance of the indicating matrix. Moreover, a pair of nodes are more likely to be aligned if most of their neighbors are aligned, motivating us to develop an iterative algorithm to refine the alignment results recursively. We conduct extensive experiments on real-world and synthetic datasets to evaluate the effectiveness of GTCAlign. The experimental results show that GTCAlign outperforms state-of-the-art graph alignment approaches.