  The high temporal variation of the point clouds is the key challenge of 3D
single-object tracking (3D SOT). Existing approaches rely on the assumption
that the shape variation of the point clouds and the motion of the objects
across neighboring frames are smooth, failing to cope with high temporal
variation data. In this paper, we present a novel framework for 3D SOT in point
clouds with high temporal variation, called HVTrack. HVTrack proposes three
novel components to tackle the challenges in the high temporal variation
scenario: 1) A Relative-Pose-Aware Memory module to handle temporal point cloud
shape variations; 2) a Base-Expansion Feature Cross-Attention module to deal
with similar object distractions in expanded search areas; 3) a Contextual
Point Guided Self-Attention module for suppressing heavy background noise. We
construct a dataset with high temporal variation (KITTI-HV) by setting
different frame intervals for sampling in the KITTI dataset. On the KITTI-HV
with 5 frame intervals, our HVTrack surpasses the state-of-the-art tracker
CXTracker by 11.3%/15.7% in Success/Precision.
