Point cloud is an important 3D data structure, but its irregular format brings great challenges
to deep learning. The advent of PointNet makes it possible to process irregular point cloud …

It is a challenge to design a deep neural network for raw point cloud, which is disordered
and unstructured data. In this paper, we introduce a cross self-attention network (CSANet) to …

Point cloud classification and segmentation are challenging tasks due to the irregular
structures, especially when there is translation variance in the point clouds. To overcome …

Recently, a lot of attention is given to deep learning on raw 3D point clouds. Existing
approaches, however, either exploit the global shape feature without paying attention to the …

With the tide of artificial intelligence, we try to apply deep learning to understand 3D data.
Point cloud is an important 3D data structure, which can accurately and directly reflect the …

Point cloud classification is a fundamental but still challenging task in 3-D computer vision.
The main issue is that learning representational features from initial point cloud objects is …

Recently, there have been some attempts of Transformer in 3D point cloud classification. In
order to reduce computations, most existing methods focus on local spatial attention, but …

The application of deep learning to 3D point clouds is challenging due to its lack of order.
Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we …

PointNet makes it possible to process point cloud data directly. However, PointNet only
extracts global features and cannot capture fine local features. How to build a refined local …

Local convolutional operation and global attention-based transformer operation can extract
features of the point cloud from two different scales respectively, but few methods can …