Point cloud registration is a fundamental technique in 3-D computer vision with applications
in graphics, autonomous driving, and robotics. However, registration tasks under …

Deformable attention only focuses on a small group of key sample-points around the
reference point and make itself be able to capture dynamically the local features of input …

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 …

Point cloud registration is a crucial task in computer vision and 3D reconstruction, aiming to
align multiple point clouds to achieve globally consistent geometric structures. However …

Point cloud registration has various applications within the computer-aided design (CAD)
community, such as model reconstruction, retrieving, and analysis. Previous approaches …

Point cloud registration plays an important role in 3-D computer vision. Local feature-based
registration as a kind of effective and robust method has two critical steps: descriptor …

Registration of point clouds collected from a pair of distant vehicles provides a
comprehensive and accurate 3D view of the driving scenario which is vital for driving safety …

Current point cloud registration methods are mainly based on local geometric information
and usually ignore the semantic information contained in the scenes. In this article, we treat …

In the context of image-to-point cloud registration, acquiring point-to-pixel correspondences
presents a challenging task since the similarity between individual points and pixels is …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …