  In a point cloud sequence, 3D object tracking aims to predict the location
and orientation of an object in the current search point cloud given a template
point cloud. Motivated by the success of transformers, we propose Point
Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D
tracking results in a coarse-to-fine manner with the help of transformer
operations. PTTR consists of three novel designs. 1) Instead of random
sampling, we design Relation-Aware Sampling to preserve relevant points to
given templates during subsampling. 2) Furthermore, we propose a Point Relation
Transformer (PRT) consisting of a self-attention and a cross-attention module.
The global self-attention operation captures long-range dependencies to enhance
encoded point features for the search area and the template, respectively.
Subsequently, we generate the coarse tracking results by matching the two sets
of point features via cross-attention. 3) Based on the coarse tracking results,
we employ a novel Prediction Refinement Module to obtain the final refined
prediction. In addition, we create a large-scale point cloud single object
tracking benchmark based on the Waymo Open Dataset. Extensive experiments show
that PTTR achieves superior point cloud tracking in both accuracy and
efficiency.
