Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …

This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It
emphasizes spatio-temporal continuity and integrates both past and future reasoning for …

Abstract 3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and
approaches in recent years, especially those under the “tracking-by-detection” paradigm …

Panoptic scene understanding and tracking of dynamic agents are essential for robots and
automated vehicles to navigate in urban environments. As LiDARs provide accurate …

Multiple object tracking gained a lot of interest from researchers in recent years, and it has
become one of the trending problems in computer vision, especially with the recent …

Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-
based autonomous driving system. It involves modeling 3D dynamic objects in complex …

Recent developments in monocular multi-object tracking have been very successful in
tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven …

Abstract 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 …