  Learning robust feature matching between the template and search area is
crucial for 3D Siamese tracking. The core of Siamese feature matching is how to
assign high feature similarity on the corresponding points between the template
and search area for precise object localization. In this paper, we propose a
novel point cloud registration-driven Siamese tracking framework, with the
intuition that spatially aligned corresponding points (via 3D registration)
tend to achieve consistent feature representations. Specifically, our method
consists of two modules, including a tracking-specific nonlocal registration
module and a registration-aided Sinkhorn template-feature aggregation module.
The registration module targets at the precise spatial alignment between the
template and search area. The tracking-specific spatial distance constraint is
proposed to refine the cross-attention weights in the nonlocal module for
discriminative feature learning. Then, we use the weighted SVD to compute the
rigid transformation between the template and search area, and align them to
achieve the desired spatially aligned corresponding points. For the feature
aggregation model, we formulate the feature matching between the transformed
template and search area as an optimal transport problem and utilize the
Sinkhorn optimization to search for the outlier-robust matching solution. Also,
a registration-aided spatial distance map is built to improve the matching
robustness in indistinguishable regions (e.g., smooth surface). Finally, guided
by the obtained feature matching map, we aggregate the target information from
the template into the search area to construct the target-specific feature,
which is then fed into a CenterPoint-like detection head for object
localization. Extensive experiments on KITTI, NuScenes and Waymo datasets
verify the effectiveness of our proposed method.
