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

Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has
emerged as a crucial aspect of 3D object detection in autonomous driving. However …

Fusing multi-modality inputs from different sensors is an effective way to improve the
performance of 3D object detection. However, current methods overlook two important …

In the field of autonomous driving, accurate and efficient 3D object detection is crucial for
ensuring safe and reliable operation. This paper focuses on the fusion of camera and LiDAR …

Multimodal object detection leverages diverse modal information to enhance the accuracy
and robustness of detectors. Due to its ability to capture long-range dependencies, the …

Person Re-Identification (Re-ID) has gained popularity in computer vision, enabling cross-
camera pedestrian recognition. Although the development of deep learning has provided a …

Existing top-performance autonomous driving systems typically rely on the multi-modal
fusion strategy for reliable scene understanding. This design is however fundamentally …

At present, the top-down approach requires the introduction of pedestrian detection
algorithms in multi-person pose estimation. In this paper, we propose an end-to-end …

The progress in object detection for autonomous driving using LiDAR point cloud data has
been remarkable. However, current voxel-based two-stage detectors have not fully …

Unmanned aerial vehicle (UAV)-based bi-modal salient object detection (BSOD) aims to
segment salient objects in a scene utilizing complementary cues in unaligned RGB and …