  Current 3D single object tracking methods primarily rely on the Siamese
matching-based paradigm, which struggles with textureless and incomplete LiDAR
point clouds. Conversely, the motion-centric paradigm avoids appearance
matching, thus overcoming these issues. However, its complex multi-stage
pipeline and the limited temporal modeling capability of a single-stream
architecture constrain its potential. In this paper, we introduce SiamMo, a
novel and simple Siamese motion-centric tracking approach. Unlike the
traditional single-stream architecture, we employ Siamese feature extraction
for motion-centric tracking. This decouples feature extraction from temporal
fusion, significantly enhancing tracking performance. Additionally, we design a
Spatio-Temporal Feature Aggregation module to integrate Siamese features at
multiple scales, capturing motion information effectively. We also introduce a
Box-aware Feature Encoding module to encode object size priors into motion
estimation. SiamMo is a purely motion-centric tracker that eliminates the need
for additional processes like segmentation and box refinement. Without whistles
and bells, SiamMo not only surpasses state-of-the-art methods across multiple
benchmarks but also demonstrates exceptional robustness in challenging
scenarios. SiamMo sets a new record on the KITTI tracking benchmark with 90.1\%
precision while maintaining a high inference speed of 108 FPS. The code will be
released at https://github.com/HDU-VRLab/SiamMo.
