  Most of 3D single object trackers (SOT) in point clouds follow the two-stream
multi-stage 3D Siamese or motion tracking paradigms, which process the template
and search area point clouds with two parallel branches, built on supervised
point cloud backbones. In this work, beyond typical 3D Siamese or motion
tracking, we propose a neat and compact one-stream transformer 3D SOT paradigm
from the novel perspective, termed as \textbf{EasyTrack}, which consists of
three special designs: 1) A 3D point clouds tracking feature pre-training
module is developed to exploit the masked autoencoding for learning 3D point
clouds tracking representations. 2) A unified 3D tracking feature learning and
fusion network is proposed to simultaneously learns target-aware 3D features,
and extensively captures mutual correlation through the flexible self-attention
mechanism. 3) A target location network in the dense bird's eye view (BEV)
feature space is constructed for target classification and regression.
Moreover, we develop an enhanced version named EasyTrack++, which designs the
center points interaction (CPI) strategy to reduce the ambiguous targets caused
by the noise point cloud background information. The proposed EasyTrack and
EasyTrack++ set a new state-of-the-art performance ($\textbf{18\%}$,
$\textbf{40\%}$ and $\textbf{3\%}$ success gains) in KITTI, NuScenes, and Waymo
while runing at \textbf{52.6fps} with few parameters (\textbf{1.3M}). The code
will be available at https://github.com/KnightApple427/Easytrack.
