Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in com...
The traditional Iterative Closest Point (ICP) algorithm often suffers from low computational accuracy and efficiency in certain scenarios. It is highly sensitive to the initial pose, has a poor ability to resist interference, and frequently becomes t...
Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point cloud pairs consequentially using a variant of the iterative closest point (ICP) algorithm, but most versions...
Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geome...
In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. I...
In order to solve the problem of the traditional iterative closest point algorithm (ICPA), which requires a high initial position of point cloud and improves the speed and accuracy of point cloud registration, a new registration method is proposed in...
Point cloud registration plays a key role in three-dimensional scene reconstruction, and determines the effect of reconstruction. The iterative closest point algorithm is widely used for point cloud registration. To improve the accuracy of point clou...
In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to re...
Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the init...
Point cloud registration is a crucial preprocessing step for point cloud data analysis and applications. Nowadays, many deep-learning-based methods have been proposed to improve the registration quality. These methods always use the sum of two cross-...
Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the sc...
Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the correspondin...
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper p...
Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve...
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an a...
This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dy...
Existing point cloud registration methods can effectively handle large-scale and partially overlapping point cloud pairs. However, registering unbalanced point cloud pairs with significant disparities in spatial extent and point density remains a cha...
When measuring surface deformation, because the overlap of point clouds before and after deformation is small and the accuracy of the initial value of point cloud registration cannot be guaranteed, traditional point cloud registration methods cannot...
Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often f...
Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and select...
Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning qual...
Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this stud...
For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracte...
In response to the issues of slow convergence and the tendency to fall into local optima in traditional iterative closest point (ICP) point cloud registration algorithms, this study presents a fast registration algorithm for laser point clouds based...
Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved throu...
ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, c...
Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discr...
This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds ge...
Point cloud registration is a critical problem because it is the basis of many 3D vision tasks. With the popularity of deep learning, many scholars have focused on leveraging deep neural networks to address the point cloud registration problem. Howev...
Point cloud registration is a technology that aligns point cloud data from different viewpoints by computing coordinate transformations to integrate them into a specified coordinate system. Many cutting-edge fields, including autonomous driving, indu...
Although researchers have investigated a variety of approaches to the development of three-dimensional (3D) point cloud matching algorithms, the results have been limited by low accuracy and slow speed when registering large numbers of point cloud da...
Point cloud registration is one of the basic research hotspots in the field of 3D reconstruction. Although many previous studies have made great progress, the registration of rock point clouds remains an ongoing challenge, due to the complex surface,...
Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global featu...
This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, thi...
Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper prese...
Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of a...
Nonrigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex nonrigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple r...
This paper proposes a heuristic reward reinforcement learning framework for point cloud registration. As an essential step of many 3D computer vision tasks such as object recognition and 3D reconstruction, point cloud registration has been well studi...
In the field of Autonomous Surface Vehicle (ASV), research on advanced perception technologies is crucial for enhancing their intelligence and autonomy. In particular, laser point cloud registration technology serves as a foundation for improving the...
The environment of ski resorts is usually complex and changeable, and there are few characteristic objects in the background, which creates many difficulties for the registration of ski-resort point cloud datasets. However, in the traditional iterati...
Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise...
Point cloud registration plays a crucial role in 3D mapping and localization. Urban scene point clouds pose significant challenges for registration due to their large data volume, similar scenarios, and dynamic objects. Estimating the location by ins...
To address the problem of point-cloud registration accuracy degradation or even failure in traditional Voxelized GICP(VGICP) under bad initial pose due to improper voxel resolution settings, this paper proposes an Adaptive Resolution VGICP (AR-VGICP)...
Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds,...
Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing com...
In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature inf...
When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the component...
Three-dimensional (3D) reconstruction is an essential technique to visualize and monitor the growth of agricultural and forestry plants. However, inspecting tall plants (trees) remains a challenging task for single-camera systems. A combination of lo...
The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registratio...
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