Human driver can easily describe the complex traffic scene by visual system. Such an ability
of precise perception is essential for driver's planning. To achieve this, a geometry-aware …

In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …

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 …

Semantic scene completion also known as semantic occupancy prediction can provide
dense geometric and semantic information for autonomous vehicles which attracts the …

In the field of 3D object detection for autonomous driving LiDAR-Camera (LC) fusion is the
top-performing sensor configuration. Still LiDAR is relatively high cost which hinders …

It is evident that the state-of-the-art in multisensory hand gesture recognition indicates the
superior performance of the A-mode ultrasound (AUS) modality over its counterparts …

Deep neural network (DNN) is extensively explored for LiDAR-based 3D object detection, a
crucial perception task in the field of autonomous driving. However, the presence of …

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

Striking a balance between precision and efficiency presents a prominent challenge in the
bird's-eye-view (BEV) 3D object detection. Although previous camera-based BEV methods …