Achieving fully autonomous driving with heightened safety and efficiency depends on
vehicle-to-everything (V2X) cooperative perception (CP), which allows vehicles to share …

This paper aims to tackle the problem of modeling dynamic urban streets for autonomous
driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to …

We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Object tracking is a fundamental computer vision problem that refers to a set of methods
proposed to precisely track the motion trajectory of an object in a video. Multiple Object …

3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection,
conducive to subsequent motion planning and navigation tasks in autonomous driving …

In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have
focused on tracking accuracy and neglected computation speed, commonly by designing …

Siamese network based trackers formulate 3D single object tracking as cross-correlation
learning between point features of a template and a search area. Due to the large …

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail
to capture the complete spatial information about the objects due to long distance and …

Current 3D single object tracking approaches track the target based on a feature
comparison between the target template and the search area. However, due to the common …