  Online 3D multi-object tracking (MOT) has witnessed significant research
interest in recent years, largely driven by demand from the autonomous systems
community. However, 3D offline MOT is relatively less explored. Labeling 3D
trajectory scene data at a large scale while not relying on high-cost human
experts is still an open research question. In this work, we propose Batch3DMOT
which follows the tracking-by-detection paradigm and represents real-world
scenes as directed, acyclic, and category-disjoint tracking graphs that are
attributed using various modalities such as camera, LiDAR, and radar. We
present a multi-modal graph neural network that uses a cross-edge attention
mechanism mitigating modality intermittence, which translates into sparsity in
the graph domain. Additionally, we present attention-weighted convolutions over
frame-wise k-NN neighborhoods as suitable means to allow information exchange
across disconnected graph components. We evaluate our approach using various
sensor modalities and model configurations on the challenging nuScenes and
KITTI datasets. Extensive experiments demonstrate that our proposed approach
yields an overall improvement of 3.3% in the AMOTA score on nuScenes thereby
setting the new state-of-the-art for 3D tracking and further enhancing false
positive filtering.
