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3D single object tracking with point clouds is a critical task in 3D computervision. Previous methods usually input the last two frames and use thepredicted box to get the template point cloud in previous frame and the searcharea point cloud in the current frame respectively, then use similarity-basedor motion-based methods to predict the current box. Although these methodsachieved good tracking performance, they ignore the historical information ofthe target, which is important for tracking. In this paper, compared toinputting two frames of point clouds, we input multi-frame of point clouds toencode the spatio-temporal information of the target and learn the motioninformation of the target implicitly, which could build the correlations amongdifferent frames to track the target in the current frame efficiently.Meanwhile, rather than directly using the point feature for feature fusion, wefirst crop the point cloud features into many patches and then use sparseattention mechanism to encode the patch-level similarity and finally fuse themulti-frame features. Extensive experiments show that our method achievescompetitive results on challenging large-scale benchmarks (62.6% in KITTI and49.66% in NuScenes).

STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking