  Siamese trackers based on 3D region proposal network (RPN) have shown
remarkable success with deep Hough voting. However, using a single seed point
feature as the cue for voting fails to produce high-quality 3D proposals.
Additionally, the equal treatment of seed points in the voting process,
regardless of their significance, exacerbates this limitation. To address these
challenges, we propose a novel transformer-based voting scheme to generate
better proposals. Specifically, a global-local transformer (GLT) module is
devised to integrate object- and patch-aware geometric priors into seed point
features, resulting in robust and accurate cues for offset learning of seed
points. To train the GLT module, we introduce an importance prediction branch
that learns the potential importance weights of seed points as a training
constraint. Incorporating this transformer-based voting scheme into 3D RPN, a
novel Siamese method dubbed GLT-T is developed for 3D single object tracking on
point clouds. Moreover, we identify that the highest-scored proposal in the
Siamese paradigm may not be the most accurate proposal, which limits tracking
performance. Towards this concern, we approach the binary score prediction task
as a ranking problem, and design a target-aware ranking loss and a
localization-aware ranking loss to produce accurate ranking of proposals. With
the ranking losses, we further present GLT-T++, an enhanced version of GLT-T.
Extensive experiments on multiple benchmarks demonstrate that our GLT-T and
GLT-T++ outperform state-of-the-art methods in terms of tracking accuracy while
maintaining a real-time inference speed. The source code will be made available
at https://github.com/haooozi/GLT-T.
