3D object tracking in point clouds is still a challenging problem due to the
sparsity of LiDAR points in dynamic environments. In this work, we propose a
Siamese voxel-to-BEV tracker, which can significantly improve the tracking
performance in sparse 3D point clouds. Specifically, it consists of a Siamese
shape-aware feature learning network and a voxel-to-BEV target localization
network. The Siamese shape-aware feature learning network can capture 3D shape
information of the object to learn the discriminative features of the object so
that the potential target from the background in sparse point clouds can be
identified. To this end, we first perform template feature embedding to embed
the template's feature into the potential target and then generate a dense 3D
shape to characterize the shape information of the potential target. For
localizing the tracked target, the voxel-to-BEV target localization network
regresses the target's 2D center and the $z$-axis center from the dense bird's
eye view (BEV) feature map in an anchor-free manner. Concretely, we compress
the voxelized point cloud along $z$-axis through max pooling to obtain a dense
BEV feature map, where the regression of the 2D center and the $z$-axis center
can be performed more effectively. Extensive evaluation on the KITTI and
nuScenes datasets shows that our method significantly outperforms the current
state-of-the-art methods by a large margin.

本文提出了一种 Siamese Voxel-to-BEV Tracker，通过 Siamese 形状感知特征学习网络和体素到 BEV 目标定位网络大幅提高了稀疏点云中的 3D 物体追踪性能，并在 KITTI 和 nuScenes 数据集上得到了显著的性能优化。