  Current LiDAR point cloud-based 3D single object tracking (SOT) methods
typically rely on point-based representation network. Despite demonstrated
success, such networks suffer from some fundamental problems: 1) It contains
pooling operation to cope with inherently disordered point clouds, hindering
the capture of 3D spatial information that is useful for tracking, a regression
task. 2) The adopted set abstraction operation hardly handles
density-inconsistent point clouds, also preventing 3D spatial information from
being modeled. To solve these problems, we introduce a novel tracking
framework, termed VoxelTrack. By voxelizing inherently disordered point clouds
into 3D voxels and extracting their features via sparse convolution blocks,
VoxelTrack effectively models precise and robust 3D spatial information,
thereby guiding accurate position prediction for tracked objects. Moreover,
VoxelTrack incorporates a dual-stream encoder with cross-iterative feature
fusion module to further explore fine-grained 3D spatial information for
tracking. Benefiting from accurate 3D spatial information being modeled, our
VoxelTrack simplifies tracking pipeline with a single regression loss.
Extensive experiments are conducted on three widely-adopted datasets including
KITTI, NuScenes and Waymo Open Dataset. The experimental results confirm that
VoxelTrack achieves state-of-the-art performance (88.3%, 71.4% and 63.6% mean
precision on the three datasets, respectively), and outperforms the existing
trackers with a real-time speed of 36 Fps on a single TITAN RTX GPU. The source
code and model will be released.
