Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse
applications across various fields, such as computer vision (CV), condition monitoring (CM) …

We present RoReg, a novel point cloud registration framework that fully exploits oriented
descriptors and estimated local rotations in the whole registration pipeline. Previous …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …

Targeting the development of smart cities to facilitate the significant analysis of large-scale
urban for construction and update. This research develops a new method named transfer …

We consider the deep point cloud analysis tasks where the inputs of the networks are
randomly rotated. Recent progress in rotation-invariant point cloud analysis is mainly driven …

Recently, various works that attempted to introduce rotation invariance to point cloud
analysis have devised point-pair features, such as angles and distances. In these methods …

Automatic identifying target multi-class objects in tunnel scenes from 3D point clouds is
widely thought to be critical for maintaining the healthy condition of the tunnel using deep …

While deep learning in 3D domain has achieved revolutionary performance in many tasks,
the robustness of these models has not been sufficiently studied or explored. Regarding the …

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success
of deep learning on images and the increased availability of 3D data. In this paper, we aim …