LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …

Radiomics is the extraction of predefined mathematic features from medical images for
predicting variables of clinical interest. Recent research has demonstrated that radiomics …

An ideal point cloud registration framework should have superior accuracy, acceptable
efficiency, and strong generalizability. However, this is highly challenging since existing …

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 …

We present Point-TTA, a novel test-time adaptation framework for point cloud registration
(PCR) that improves the generalization and the performance of registration models. While …

Machine-learning models based on a point-cloud representation of a physical object are
ubiquitous in scientific applications and particularly well-suited to the atomic-scale …

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 …

Correspondence-based point cloud registration (PCR) plays a key role in robotics and
computer vision. However, challenges like sensor noises, object occlusions, and descriptor …

Extracting geometric features from 3D point clouds is widely applied in many tasks, including
registration and recognition. We propose a simple yet effective method, termed height …