We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …

Human-centric scene understanding is significant for real-world applications, but it is
extremely challenging due to the existence of diverse human poses and actions, complex …

LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense
LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often …

Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR
sensors presents a significant scaling-up challenge. While recent efforts have explored deep …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

Labelling LiDAR point clouds for training autonomous driving is extremely expensive and
difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and …

Generative modeling of 3D LiDAR data is an emerging task with promising applications for
autonomous mobile robots, such as scalable simulation, scene manipulation, and sparse-to …

We consider the task of traffic scene generation. A common approach in the self-driving
industry is to use manual creation to generate scenes with specific characteristics and …

Radar is widely employed in many applications, especially in autonomous driving. At
present, radars are only designed as simple data collectors, and they are unable to meet …

The imbalanced distribution of different object categories poses a challenge for training
accurate object recognition models in driving scenes. Supervised machine learning models …