  With the prevalence of LiDAR sensors in autonomous driving, 3D object
tracking has received increasing attention. In a point cloud sequence, 3D
object tracking aims to predict the location and orientation of an object in
consecutive frames given an object template. 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 the given template during subsampling. 2) We propose a Point
Relation Transformer for effective feature aggregation and feature matching
between the template and search region. 3) Based on the coarse tracking
results, we employ a novel Prediction Refinement Module to obtain the final
refined prediction through local feature pooling. In addition, motivated by the
favorable properties of the Bird's-Eye View (BEV) of point clouds in capturing
object motion, we further design a more advanced framework named PTTR++, which
incorporates both the point-wise view and BEV representation to exploit their
complementary effect in generating high-quality tracking results. PTTR++
substantially boosts the tracking performance on top of PTTR with low
computational overhead. Extensive experiments over multiple datasets show that
our proposed approaches achieve superior 3D tracking accuracy and efficiency.
