Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

Existing top-performance 3D object detectors typically rely on the multi-modal fusion
strategy. This design is however fundamentally restricted due to overlooking the modality …

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 …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the
research of category-level human-object interaction. HOI4D consists of 2.4 M RGB-D …

Autonomous driving perception has made significant strides in recent years, but accurately
sensing the environment using a single sensor remains a daunting task. This review offers a …

Recently, fusing the LiDAR point cloud and camera image to improve the performance and
robustness of 3D object detection has received more and more attention, as these two …

Abstract 3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in
autonomous driving. Current approaches all follow the Siamese paradigm based on …

Robust 3D object detection is critical for safe autonomous driving. Camera and radar
sensors are synergistic as they capture complementary information and work well under …