Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

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

With the rapid advances in autonomous driving, it becomes critical to equip its sensing
system with more holistic 3D perception. However, widely explored tasks like 3D detection …

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

Safety-critical 3D scene understanding tasks necessitate not only accurate but also
confident predictions from 3D perception models. This study introduces Calib3D, a …

Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often
incorporate specifically designed inductive bias derived from benchmarks originating from …

The reliability of driving perception systems under unprecedented conditions is crucial for
practical usage. Latest advancements have prompted increasing interest in multi-LiDAR …

In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is
crucial for understanding complex 3D environments. Traditional approaches often rely on …

Foundation models have indeed made a profound impact on various fields, emerging as
pivotal components that significantly shape the capabilities of intelligent systems. In the …

The robustness of driving perception systems under unprecedented conditions is crucial for
safety-critical usages. Latest advancements have prompted increasing interests towards …