Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

With the rapid development of society and the economy, autonomous driving techniques are
widely applied in many areas, such as autonomous vehicles, autonomous drones, and …

We propose octree-based transformers, named OctFormer, for 3D point cloud learning.
OctFormer can not only serve as a general and effective backbone for 3D point cloud …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

We propose 3DETR, an end-to-end Transformer based object detection model for 3D point
clouds. Compared to existing detection methods that employ a number of 3D-specific …

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 …

Traffic signs are one of the most important information that guide cars to travel, and the
detection of traffic signs is an important component of autonomous driving and intelligent …

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 …

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

Pretraining on large labeled datasets is a prerequisite to achieve good performance in many
computer vision tasks like image recognition, video understanding etc. However, pretraining …