  In recent years, the field of autonomous driving has witnessed remarkable
advancements, driven by the integration of a multitude of sensors, including
cameras and LiDAR systems, in different prototypes. However, with the
proliferation of sensor data comes the pressing need for more sophisticated
information processing techniques. This research paper introduces a novel
modification to an object detection network that uses camera and lidar
information, incorporating an additional branch designed for the task of
re-identifying objects across adjacent cameras within the same vehicle while
elevating the quality of the baseline 3D object detection outcomes. The
proposed methodology employs a two-step detection pipeline: initially, an
object detection network is employed, followed by a 3D box estimator that
operates on the filtered point cloud generated from the network's detections.
Extensive experimental evaluations encompassing both 2D and 3D domains validate
the effectiveness of the proposed approach and the results underscore the
superiority of this method over traditional Non-Maximum Suppression (NMS)
techniques, with an improvement of more than 5\% in the car category in the
overlapping areas.
