  3D single object tracking is a key task in 3D computer vision. However, the
sparsity of point clouds makes it difficult to compute the similarity and
locate the object, posing big challenges to the 3D tracker. Previous works
tried to solve the problem and improved the tracking performance in some common
scenarios, but they usually failed in some extreme sparse scenarios, such as
for tracking objects at long distances or partially occluded. To address the
above problems, in this letter, we propose a sparse-to-dense and
transformer-based framework for 3D single object tracking. First, we transform
the 3D sparse points into 3D pillars and then compress them into 2D BEV
features to have a dense representation. Then, we propose an attention-based
encoder to achieve global similarity computation between template and search
branches, which could alleviate the influence of sparsity. Meanwhile, the
encoder applies the attention on multi-scale features to compensate for the
lack of information caused by the sparsity of point cloud and the single scale
of features. Finally, we use set-prediction to track the object through a
two-stage decoder which also utilizes attention. Extensive experiments show
that our method achieves very promising results on the KITTI and NuScenes
datasets.
