scholarly journals SCRnet: A Spatial Consistency Guided Network Using Contrastive Learning for Point Cloud Registration

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 140
Author(s):  
Huixiang Shao ◽  
Zhijiang Zhang ◽  
Xiaoyu Feng ◽  
Dan Zeng
Keyword(s):  
Point Cloud ◽  
Target Point ◽  
Outlier Rejection ◽  
Initial Alignment ◽  
Confidence Estimation ◽  
Global Features ◽  
Point Cloud Registration ◽  
Spatial Consistency ◽  
Main Challenge ◽  
Four Blocks

Point cloud registration is used to find a rigid transformation from the source point cloud to the target point cloud. The main challenge in the point cloud registration is in finding correct correspondences in complex scenes that may contain many noise and repetitive structures. At present, many existing methods use outlier rejections to help the network obtain more accurate correspondences, but they often ignore the spatial consistency between keypoints. Therefore, to address this issue, we propose a spatial consistency guided network using contrastive learning for point cloud registration (SCRnet), in which its overall stage is symmetrical. SCRnet consists of four blocks, namely feature extraction block, confidence estimation block, contrastive learning block and registration block. Firstly, we use mini-PointNet to extract coarse local and global features. Secondly, we propose confidence estimation block, which formulate outlier rejection as confidence estimation problem of keypoint correspondences. In addition, the local spatial features are encoded into the confidence estimation block, which makes the correspondence possess local spatial consistency. Moreover, we propose contrastive learning block by constructing positive point pairs and hard negative point pairs and using Point-Pair-INfoNCE contrastive loss, which can further remove hard outliers through global spatial consistency. Finally, the proposed registration block selects a set of matching points with high spatial consistency and uses these matching sets to calculate multiple transformations, then the best transformation can be identified by initial alignment and Iterative Closest Point (ICP) algorithm. Extensive experiments are conducted on KITTI and nuScenes dataset, which demonstrate the high accuracy and strong robustness of SCRnet on point cloud registration task.

scholarly journals 3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

2018 ◽  
Vol 8 (10) ◽  
pp. 1776 ◽  
Author(s):  
Jian Liu ◽  
Di Bai ◽  
Li Chen
Keyword(s):  
Machine Vision ◽  
Point Cloud ◽  
Three Dimensional ◽  
Target Object ◽  
Initial Alignment ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Feature Information ◽  
Complex Target

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 information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

scholarly journals PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

2021 ◽  
Author(s):  
Xuyang Bai ◽  
Zixin Luo ◽  
Lei Zhou ◽  
Hongkai Chen ◽  
Lei Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Cloud Registration ◽  
Spatial Consistency

scholarly journals Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration

2021 ◽  
Author(s):  
Haobo Jiang ◽  
Jianjun Qian ◽  
Jin Xie ◽  
Jian Yang
Keyword(s):  
Dynamic Model ◽  
Point Cloud ◽  
Fundamental Problem ◽  
Point Clouds ◽  
Search Space ◽  
Target Point ◽  
Planning Problem ◽  
Point Cloud Registration ◽  
Registration Process ◽  
Cross Entropy Method

Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which can seek the transformation between the source and target point clouds through trial and error. By modeling the point cloud registration process as a Markov decision process (MDP), we develop a latent dynamic model of point clouds, consisting of a transformation network and evaluation network. The transformation network aims to predict the new transformed feature of the point cloud after performing a rigid transformation (i.e., action) on it while the evaluation network aims to predict the alignment precision between the transformed source point cloud and target point cloud as the reward signal. Once the dynamic model of the point cloud is trained, we employ the cross-entropy method (CEM) to iteratively update the planning policy by maximizing the rewards in the point cloud registration process. Thus, the optimal policy, i.e., the transformation between the source and target point clouds, can be obtained via gradually narrowing the search space of the transformation. Experimental results on ModelNet40 and 7Scene benchmark datasets demonstrate that our method can yield good registration performance in an unsupervised manner.

scholarly journals A point cloud registration algorithm based on normal vector and particle swarm optimization

2019 ◽  
Vol 53 (3-4) ◽  
pp. 265-275 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Heng Li ◽  
Yangmin Li ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Target Point ◽  
Normal Vector ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization ◽  
Registration Algorithm ◽  
The Right

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

scholarly journals Deep Global Features for Point Cloud Alignment

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4032
Author(s):  
Ahmed El Khazari ◽  
Yue Que ◽  
Thai Leang Sung ◽  
Hyo Jong Lee
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Local Optimum ◽  
Global Features ◽  
Point Cloud Registration ◽  
Local Optima ◽  
Existing Problems ◽  
Proposed Model ◽  
Auxiliary Module ◽  
Feature Alignment

Point cloud registration is a key problem in computer vision applications and involves finding a rigid transform from a point cloud into another such that they align together. The iterative closest point (ICP) method is a simple and effective solution that converges to a local optimum. However, despite the fact that point cloud registration or alignment is addressed in learning-based methods, such as PointNetLK, they do not offer good generalizability for point clouds. In this stud, we proposed a learning-based approach that addressed existing problems, such as finding local optima for ICP and achieving minimum generalizability. The proposed model consisted of three main parts: an encoding network, an auxiliary module that weighed the contribution of each input point cloud, and feature alignment to achieve the final transform. The proposed architecture offered greater generalization among the categories. Experiments were performed on ModelNet40 with different configurations and the results indicated that the proposed approach significantly outperformed the state-of-the-art point cloud alignment methods.

scholarly journals Efficient Rock Mass Point Cloud Registration Based on Local Invariants

2021 ◽  
Vol 13 (8) ◽  
pp. 1540
Author(s):  
Yunbiao Wang ◽  
Jun Xiao ◽  
Lupeng Liu ◽  
Ying Wang
Keyword(s):  
Point Cloud ◽  
Basic Research ◽  
Complex Surface ◽  
Point Clouds ◽  
Target Point ◽  
Translation Vector ◽  
Point Sets ◽  
Point Cloud Registration ◽  
Registration Method ◽  
Local Invariants

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, arbitrary shape, and high resolution of rock masses. To overcome these challenges, a novel registration method for rock point clouds, based on local invariants, is proposed in this paper. First, to handle the massive point clouds, a point of interest filtering method based on a sum vector is adopted to reduce the number of points. Second, the remaining points of interest are divided into several cluster point sets and the centroid of each cluster is calculated. Then, we determine the correspondence between the original point cloud and the target point cloud by proving the inherent similarity (using the trace of the covariance matrix) of the remaining point sets. Finally, the rotation matrix and translation vector are calculated, according to the corresponding centroids, and a correction method is used to adjust the positions of the centroids. To illustrate the superiority of our method, in terms of accuracy and efficiency, we conducted experiments on multiple datasets. The experimental results show that the method has higher accuracy (about ten times) and efficiency than similar existing methods.

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm
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