  3D single object tracking with point clouds is a critical task in 3D computer
vision. Previous methods usually input the last two frames and use the
predicted box to get the template point cloud in previous frame and the search
area point cloud in the current frame respectively, then use similarity-based
or motion-based methods to predict the current box. Although these methods
achieved good tracking performance, they ignore the historical information of
the target, which is important for tracking. In this paper, compared to
inputting two frames of point clouds, we input multi-frame of point clouds to
encode the spatio-temporal information of the target and learn the motion
information of the target implicitly, which could build the correlations among
different frames to track the target in the current frame efficiently.
Meanwhile, rather than directly using the point feature for feature fusion, we
first crop the point cloud features into many patches and then use sparse
attention mechanism to encode the patch-level similarity and finally fuse the
multi-frame features. Extensive experiments show that our method achieves
competitive results on challenging large-scale benchmarks (62.6% in KITTI and
49.66% in NuScenes).
