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 …

Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …

In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …

We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR
point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant …

Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks
such as image and video, exploring MAE in large-scale 3D point clouds remains …

Nowadays, vehicles with a high level of automation are being driven everywhere. With the
apparent success of autonomous driving technology, we keep working to achieve fully …

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 …

Recent advance in 2D CNNs has revealed that large kernels are important. However, when
directly applying large convolutional kernels in 3D CNNs, severe difficulties are met, where …

This paper tries to address a fundamental question in point cloud self-supervised learning:
what is a good signal we should leverage to learn features from point clouds without …

LiDAR and cameras are complementary sensors for 3D object detection in autonomous
driving. However, it is challenging to explore the unnatural interaction between point clouds …