Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

The research community has increasing interest in autonomous driving research, despite
the resource intensity of obtaining representative real world data. Existing self-driving …

Existing datasets for autonomous driving (AD) often lack diversity and long-range
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …

Accurate detection of 3D objects is a fundamental problem in computer vision and has an
enormous impact on autonomous cars, augmented/virtual reality and many applications in …

In autonomous driving perception systems, 3D detection and tracking are the two
fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D …

With the increasing global popularity of self-driving cars, there is an immediate need for
challenging real-world datasets for benchmarking and training various computer vision tasks …

Vehicle 3D extents and trajectories are critical cues for predicting the future location of
vehicles and planning future agent ego-motion based on those predictions. In this paper, we …

While separately leveraging monocular 3D object detection and 2D multi-object tracking can
be straightforwardly applied to sequence images in a frame-by-frame fashion, stand-alone …

Cooperative perception offers several benefits for enhancing the capabilities of autonomous
vehicles and improving road safety. Using roadside sensors in addition to onboard sensors …

We present an end-to-end method for object detection and trajectory prediction utilizing multi-
view representations of LiDAR returns. Our method builds on a state-of-the-art Bird's-Eye …