  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.
