论文标题
Polarmot：几何关系可以将我们带到3D多对象跟踪中多远？
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
论文作者
论文摘要
大多数（3D）多对象跟踪方法依赖于数据关联的外观提示。相比之下，我们研究了仅通过编码3D空间中对象之间的几何关系作为数据驱动数据关联的线索，我们才能达到多远。我们将3D检测编码为图中的节点，其中对象之间的空间和时间成对关系是通过图边缘上的局部极性坐标编码的。这种表示使我们的几何关系不变到全球变换和平滑的轨迹变化，尤其是在非全面运动下。这使我们的图形神经网络可以学会有效地编码时间和空间相互作用，并充分利用上下文和运动提示，以通过将数据关联作为边缘分类来获得最终的场景解释。我们在Nuscenes数据集上建立了一个新的最先进的方法，更重要的是，我们的方法在不同位置（波士顿，新加坡，Karlsruhe）和数据集（Nuscenes和Kitti）中跨越了我们的方法。
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).
