Most (3D) multi-object tracking methods rely on appearance-based cues for
data association. By contrast, we investigate how far we can get by only
encoding geometric relationships between objects in 3D space as cues for
data-driven data association. We encode 3D detections as nodes in a graph,
where spatial and temporal pairwise relations among objects are encoded via
localized polar coordinates on graph edges. This representation makes our
geometric relations invariant to global transformations and smooth trajectory
changes, especially under non-holonomic motion. This allows our graph neural
network to learn to effectively encode temporal and spatial interactions and
fully leverage contextual and motion cues to obtain final scene interpretation
by posing data association as edge classification. We establish a new
state-of-the-art on nuScenes dataset and, more importantly, show that our
method, PolarMOT, generalizes remarkably well across different locations
(Boston, Singapore, Karlsruhe) and datasets (nuScenes and KITTI).

该研究提出了一种基于几何关系的多目标跟踪方法，通过将 3D 检测编码为图中的节点，并利用图边上的局部极坐标编码表征物体之间的空间和时间关系。在多项测试中，该方法显著超越了现有技术的水平，并在不同的场景和数据集上表现出了很好的泛化能力。