Abstract Three-dimensional (3D) point cloud registration is a fundamental step for many 3D
modeling and mapping applications. Existing approaches are highly disparate in the data …

We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

Despite recent success in incorporating learning into point cloud registration, many works
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …

As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to
seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration …

In this paper, we present a second order spatial compatibility (SC^ 2) measure based
method for efficient and robust point cloud registration (PCR), called SC^ 2-PCR. Firstly, we …

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …

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 study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods have shown great potential through bypassing the detection …