In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Current research is primarily dedicated to advancing the accuracy of camera-only 3D object
detectors (apprentice) through the knowledge transferred from LiDAR-or multi-modal-based …

To achieve accurate and low-cost 3D object detection, existing methods propose to benefit
camera-based multi-view detectors with spatial cues provided by the LiDAR modality, eg …

The inherent noisy and sparse characteristics of radar data pose challenges in finding
effective representations for 3D object detection. In this paper we propose RadarDistill a …

Abstract Bird's-eye-view (BEV) grid is a common representation for the perception of road
components, eg, drivable area, in autonomous driving. Most existing approaches rely on …

We present DejaVu, a novel framework which leverages conditional image regeneration as
additional supervision during training to improve deep networks for dense prediction tasks …

The development of self-driving or autonomous vehicles has led to significant
advancements in 3D object detection technologies, which are critical for the safety and …

Recent advancements in camera-based 3D object detection have introduced cross-modal
knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging …

Low-cost, vision-centric 3D perception systems for autonomous driving have made
significant progress in recent years, narrowing the gap to expensive LiDAR-based methods …

Semantic Scene Completion (SSC) is pivotal in autonomous driving perception, frequently
confronted with the complexities of weather and illumination changes. The long-term …