Existing point cloud modeling datasets primarily express the modeling precision by pose or
trajectory precision rather than the point cloud modeling effect itself. Under this demand, we …

Traditional manual and semi-automatic approaches rely heavily on surveying control points
and manually picking equivalent point pairs, which is time-consuming and labor-intensive …

The convolutional neural networks (CNNs) functioning on geometric learning for the urban
large-scale 3D meshes are indispensable because of their substantial, complex, and …

In many applications related to point clouds, registration is an inevitable step when
processing point cloud data. The registration methods performed by local shape descriptor …

Estimating the rigid transformation between two LiDAR scans through putative 3D
correspondences is a typical point cloud registration paradigm. Current 3D feature matching …

Point set registration is an essential technique in the field of machine vision. In this article,
we propose a robust global optimal solution to for the point set registration of feature points …

To mitigate the impact of mixed pixels in hyperspectral images (HSIs), substantial progress
has been made in both model-and deep-learning-based unmixing methods. However, the …

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with
applications encompassing object classification, part segmentation, and point cloud …

Investigating underwater environments using Multi-Beam Echo Sounder (MBES) point cloud
registration technology is a critical yet underdeveloped area in oceanographic research …

Point cloud registration is challenging in the presence of heavy outlier correspondences.
This paper focuses on addressing the robust correspondence-based registration problem …