Category-specific models are provenly valuable methods in 3D single object
tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such
over-specialized model designs incur redundant parameters, thus limiting the
broader applicability of 3D SOT task. This paper first introduces unified
models that can simultaneously track objects across all categories using a
single network with shared model parameters. Specifically, we propose to
explicitly encode distinct attributes associated to different object
categories, enabling the model to adapt to cross-category data. We find that
the attribute variances of point cloud objects primarily occur from the varying
size and shape (e.g., large and square vehicles v.s. small and slender humans).
Based on this observation, we design a novel point set representation learning
network inheriting transformer architecture, termed AdaFormer, which adaptively
encodes the dynamically varying shape and size information from cross-category
data in a unified manner. We further incorporate the size and shape prior
derived from the known template targets into the model's inputs and learning
objective, facilitating the learning of unified representation. Equipped with
such designs, we construct two category-unified models SiamCUT and
MoCUT.Extensive experiments demonstrate that SiamCUT and MoCUT exhibit strong
generalization and training stability. Furthermore, our category-unified models
outperform the category-specific counterparts by a significant margin (e.g., on
KITTI dataset, 12% and 3% performance gains on the Siamese and motion
paradigms). Our code will be available.

通过引入统一模型从不同角度研究 3D 对象跟踪，本文针对点云对象的属性差异设计了 AdaFormer 网络，并在学习目标中加入了已知模板目标的大小和形状先验知识，提高了模型的推广能力。结果表明，统一模型在 KITTI 数据集上相对于特定类别模型在 Siamese 和 motion 范例上分别提高了 12% 和 3% 的性能。