Robotic perception requires the modeling of both 3D geometry and semantics. Existing
methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details …

End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …

High-Definition (HD) maps are essential for the safety of autonomous driving systems. While
existing techniques employ camera images and onboard sensors to generate vectorized …

Comprehensive modeling of the surrounding 3D world is crucial for the success of
autonomous driving. However existing perception tasks like object detection road structure …

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

Recently 3D object detection from surround-view images has made notable advancements
with its low deployment cost. However, most works have primarily focused on close …

Long-term temporal fusion is a crucial but often overlooked technique in camera-based
Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner …

The field of autonomous driving increasingly demands high-quality annotated training data.
In this paper we propose Panacea an innovative approach to generate panoramic and …

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 …

In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …