  Point cloud-based 3D object tracking is an important task in autonomous
driving. Though great advances regarding Siamese-based 3D tracking have been
made recently, it remains challenging to learn the correlation between the
template and search branches effectively with the sparse LIDAR point cloud
data. Instead of performing correlation of the two branches at just one point
in the network, in this paper, we present a multi-correlation Siamese
Transformer network that has multiple stages and carries out feature
correlation at the end of each stage based on sparse pillars. More
specifically, in each stage, self-attention is first applied to each branch
separately to capture the non-local context information. Then, cross-attention
is used to inject the template information into the search area. This strategy
allows the feature learning of the search area to be aware of the template
while keeping the individual characteristics of the template intact. To enable
the network to easily preserve the information learned at different stages and
ease the optimization, for the search area, we densely connect the initial
input sparse pillars and the output of each stage to all subsequent stages and
the target localization network, which converts pillars to bird's eye view
(BEV) feature maps and predicts the state of the target with a small densely
connected convolution network. Deep supervision is added to each stage to
further boost the performance as well. The proposed algorithm is evaluated on
the popular KITTI, nuScenes, and Waymo datasets, and the experimental results
show that our method achieves promising performance compared with the
state-of-the-art. Ablation study that shows the effectiveness of each component
is provided as well. Code is available at
https://github.com/liangp/MCSTN-3DSOT.
