Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

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 …

LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive
safely. Given the limited hardware resources, existing 3D perception models are not able to …

Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …

Most prior work represents the shapes of point clouds by coordinates. However, it is
insufficient to describe the local geometry directly. In this paper, we present RepSurf …

Recently, directly detecting 3D objects from 3D point clouds has received increasing
attention. To extract object representation from an irregular point cloud, existing methods …

Many adaptations of transformers have emerged to address the single-modal vision tasks,
where self-attention modules are stacked to handle input sources like images. Intuitively …

Existing top-performance 3D object detectors typically rely on the multi-modal fusion
strategy. This design is however fundamentally restricted due to overlooking the modality …

The rapid progress in 3D scene understanding has come with growing demand for data;
however, collecting and annotating 3D scenes (eg point clouds) are notoriously hard. For …