3D LiDAR-based single object tracking (SOT) has gained increasing attention
as it plays a crucial role in 3D applications such as autonomous driving. The
central problem is how to learn a target-aware representation from the sparse
and incomplete point clouds. In this paper, we propose a novel Correlation
Pyramid Network (CorpNet) with a unified encoder and a motion-factorized
decoder. Specifically, the encoder introduces multi-level self attentions and
cross attentions in its main branch to enrich the template and search region
features and realize their fusion and interaction, respectively. Additionally,
considering the sparsity characteristics of the point clouds, we design a
lateral correlation pyramid structure for the encoder to keep as many points as
possible by integrating hierarchical correlated features. The output features
of the search region from the encoder can be directly fed into the decoder for
predicting target locations without any extra matcher. Moreover, in the decoder
of CorpNet, we design a motion-factorized head to explicitly learn the
different movement patterns of the up axis and the x-y plane together.
Extensive experiments on two commonly-used datasets show our CorpNet achieves
state-of-the-art results while running in real-time.

本文提出了一种新颖的相关金字塔网络（CorpNet），采用统一的编码器和运动分解解码器，着重解决了如何从稀疏不完整的点云中学习目标感知表征的中心问题。经过实验证明，该方法在两个常用数据集上均取得了最先进的结果，并且具有实时性。