LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich 3D information, and stability in harsh environments. Place …

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 …

We investigate the problem of 3D point clouds semantic segmentation. Recently, a large
amount of research work has focused on local feature aggregation. However, the …

The main solution for large-scale point clouds registration is to first obtain a set of matched
3D keypoint pairs and then accomplish the point cloud registration task based on these …

This paper presents a 3D registration method with maximal cliques (MAC) for 3D point cloud
registration (PCR). The key insight is to loosen the previous maximum clique constraint and …

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 …

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 …

Point cloud registration aims to estimate a transformation that aligns point clouds collected
from different perspectives. In learning-based point cloud registration, a robust descriptor 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 …