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

The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and providing …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …

Existing offboard 3D detectors always follow a modular pipeline design to take advantage of
unlimited sequential point clouds. We have found that the full potential of offboard 3D …

This paper aims for high-performance offline LiDAR-based 3D object detection. We first
observe that experienced human annotators annotate objects from a track-centric …

Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D
space in autonomous driving scenarios. However objects in the BEV representation typically …

A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …