  Siamese network based trackers formulate 3D single object tracking as
cross-correlation learning between point features of a template and a search
area. Due to the large appearance variation between the template and search
area during tracking, how to learn the robust cross correlation between them
for identifying the potential target in the search area is still a challenging
problem. In this paper, we explicitly use Transformer to form a 3D Siamese
Transformer network for learning robust cross correlation between the template
and the search area of point clouds. Specifically, we develop a Siamese point
Transformer network to learn shape context information of the target. Its
encoder uses self-attention to capture non-local information of point clouds to
characterize the shape information of the object, and the decoder utilizes
cross-attention to upsample discriminative point features. After that, we
develop an iterative coarse-to-fine correlation network to learn the robust
cross correlation between the template and the search area. It formulates the
cross-feature augmentation to associate the template with the potential target
in the search area via cross attention. To further enhance the potential
target, it employs the ego-feature augmentation that applies self-attention to
the local k-NN graph of the feature space to aggregate target features.
Experiments on the KITTI, nuScenes, and Waymo datasets show that our method
achieves state-of-the-art performance on the 3D single object tracking task.
