Online high-definition (HD) map construction is an important and challenging task in
autonomous driving. Recently, there has been a growing interest in cost-effective multi-view …

In the realm of autonomous driving, accurate 3D perception is the foundation. However,
develo** such models relies on extensive human annotations–a process that is both …

Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely
heavily on illumination conditions and often fail to capture accurate 3D information, while …

Vision-centric autonomous driving has demonstrated excellent performance with
economical sensors. As the fundamental step, 3D perception aims to infer 3D information …

Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly
in tasks like 3D object detection and HD map construction. These models provide essential …

LiDAR data pretraining offers a promising approach to leveraging large-scale, readily
available datasets for enhanced data utilization. However, existing methods predominantly …

Identifying affordance regions on 3D objects from semantic cues is essential for robotics and
human-machine interaction. However, existing 3D affordance learning methods struggle …

Recent advancements in vision foundation models (VFMs) have revolutionized visual
perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous …

Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use
for autonomous driving, particularly in generating interpretable driving decisions through …

Deep hashing has been widely used for large-scale approximate nearest neighbor search
due to its storage and search efficiency. However, existing deep hashing methods …