Three-dimensional skeleton-based action recognition (3D SAR) has gained important
attention within the computer vision community, owing to the inherent advantages offered by …

Deep point cloud registration methods face challenges to partial overlaps and rely on
labeled data. To address these issues, we propose UDPReg, an unsupervised deep …

Photorealistic 3D models are important data sources for digital twin cities and smart city
applications. These models are usually generated from data collected by aerial or ground …

Novel class discovery (NCD) for semantic segmentation is the task of learning a model that
can segment unlabelled (novel) classes using only the supervision from labelled (base) …

For point cloud registration (PCR), a matching matrix is critical. Unfortunately, the existing
approaches do not explicitly devise schemes to refine the matching matrix. Furthermore …

Point cloud registration (PCR) involves determining a rigid transformation that aligns one
point cloud to another. Despite the plethora of outstanding deep learning (DL)-based …

As a prerequisite for many 3D visualization tasks, point cloud registration has a wide range
of applications in 3D scene reconstruction, pose estimation, navigation, and remote sensing …

Point cloud registration is a critical research area in computer vision with extensive
applications. Recent studies have unveiled the significant potential of graph neural networks …

Abstract The task of Novel Class Discovery (NCD) in semantic segmentation involves
training a model to accurately segment unlabelled (novel) classes, using the supervision …

Abstract 3D point cloud registration is a crucial task in computer vision, robotics, and
safeguarding cultural artifacts in the digital realm. However, maintaining a balance between …