Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Abstract Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses
RGB images and LiDAR data by depth completion has gained great attention. However …

Current perception models in autonomous driving have become notorious for greatly relying
on a mass of annotated data to cover unseen cases and address the long-tail problem. On …

Rapid progress in 3D semantic segmentation is inseparable from the advances of deep
network models, which highly rely on large-scale annotated data for training. To address the …

Advanced 3D object detection methods usually rely on large-scale, elaborately labeled
datasets to achieve good performance. However, labeling the bounding boxes for the 3D …

Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely
heavily on the huge amount of accurately labeled samples, however, 3D annotation in the …

It is quite laborious and costly to manually label LiDAR point cloud data for training high-
quality 3D object detectors. This work proposes a weakly supervised framework which …

Semi-supervised video action recognition tends to enable deep neural networks to achieve
remarkable performance even with very limited labeled data. However, existing methods are …

Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to
comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing …