Medical image registration is a fundamental and critical task in medical image analysis. With
the rapid development of deep learning, convolutional neural networks (CNNs) have …

Feature extraction is a key step for deep-learning-based point cloud registration. In the
correspondence-free point cloud registration task, the previous work commonly aggregates …

Since real-world training datasets cannot properly sample the long tail of the underlying data
distribution, corner cases and rare out-of-domain samples can severely hinder the …

The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant
challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D …

In point cloud generation and completion, previous methods for transforming latent features
to point clouds are generally based on fully connected layers (FC-based) or folding …

Domain generalization (DG) is one of the critical issues for deep learning in unknown
domains. How to effectively represent domain-invariant context (DIC) is a difficult problem …

The success of transformer networks in the natural language processing and 2D vision
domains has encouraged the adaptation of transformers to 3D computer vision tasks …

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC)
bring significant challenges to its high-efficient compression. This paper proposes a novel …

This paper proposes a novel 3D point cloud segmentation network called EDGCNet.
Structurally, the network combines the encoder–decoder structure and graph convolution to …

Recently, the advancement of 3D point clouds in deep learning has attracted intensive
research in different application domains such as computer vision and robotic tasks …