  We propose a method for joint detection and tracking of multiple objects in
3D point clouds, a task conventionally treated as a two-step process comprising
object detection followed by data association. Our method embeds both steps
into a single end-to-end trainable network eliminating the dependency on
external object detectors. Our model exploits temporal information employing
multiple frames to detect objects and track them in a single network, thereby
making it a utilitarian formulation for real-world scenarios. Computing
affinity matrix by employing features similarity across consecutive point cloud
scans forms an integral part of visual tracking. We propose an attention-based
refinement module to refine the affinity matrix by suppressing erroneous
correspondences. The module is designed to capture the global context in
affinity matrix by employing self-attention within each affinity matrix and
cross-attention across a pair of affinity matrices. Unlike competing
approaches, our network does not require complex post-processing algorithms,
and processes raw LiDAR frames to directly output tracking results. We
demonstrate the effectiveness of our method on the three tracking benchmarks:
JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our
model to generalize well across datasets.
