scholarly journals Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7768
Author(s):  
Kyoungmin Han ◽  
Kyujin Jung ◽  
Jaeho Yoon ◽  
Minsik Lee
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Mathematical Optimization ◽  
Superior Performance ◽  
Natural Phenomenon ◽  
Metallic Surface ◽  
K Nearest Neighbor ◽  
Local Surface ◽  
Restriction Method ◽  
Repulsive Forces

3D point cloud resampling based on computational geometry is still a challenging problem. In this paper, we propose a point cloud resampling algorithm inspired by the physical characteristics of the repulsion forces between point electrons. The points in the point cloud are considered as electrons that reside on a virtual metallic surface. We iteratively update the positions of the points by simulating the electromagnetic forces between them. Intuitively, the input point cloud becomes evenly distributed by the repulsive forces. We further adopt an acceleration and damping terms in our simulation. This system can be viewed as a momentum method in mathematical optimization and thus increases the convergence stability and uniformity performance. The net force of the repulsion forces may contain a normal directional force with respect to the local surface, which can make the point diverge from the surface. To prevent this, we introduce a simple restriction method that limits the repulsion forces between the points to an approximated local plane. This approach mimics the natural phenomenon in which positive electrons cannot escape from the metallic surface. However, this is still an approximation because the surfaces are often curved rather than being strict planes. Therefore, we project the points to the nearest local surface after the movement. In addition, we approximate the net repulsion force using the K-nearest neighbor to accelerate our algorithm. Furthermore, we propose a new measurement criterion that evaluates the uniformity of the resampled point cloud to compare the proposed algorithm with baselines. In experiments, our algorithm demonstrates superior performance in terms of uniformization, convergence, and run-time.

scholarly journals KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation

2021 ◽  
Vol 13 (5) ◽  
pp. 1003
Author(s):  
Nan Luo ◽  
Hongquan Yu ◽  
Zhenfeng Huo ◽  
Jinhui Liu ◽  
Quan Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
K Nearest Neighbor ◽  
Topological Graph ◽  
Convolutional Network ◽  
Cloud Data ◽  
Nearest Neighbor Searching ◽  
Point Cloud Segmentation ◽  
Local Feature Extraction

Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

2018 ◽  
Vol 35 (16) ◽  
pp. 2757-2765 ◽  
Author(s):  
Balachandran Manavalan ◽  
Shaherin Basith ◽  
Tae Hwan Shin ◽  
Leyi Wei ◽  
Gwang Lee
Keyword(s):  
Nearest Neighbor ◽  
Feature Representation ◽  
Superior Performance ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Support Vector ◽  
Pharmaceutical Drugs ◽  
K Nearest Neighbor ◽  
Feature Descriptors ◽  
Predicted Probability

AbstractMotivationCardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.ResultsIn this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6–7% in both benchmarking and independent datasets.Availability and implementationThe user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals A GROSS ERROR ELIMINATION METHOD FOR POINT CLOUD DATA BASED ON KD-TREE

2018 ◽  
Vol XLII-3 ◽  
pp. 719-722 ◽  
Author(s):  
Q. Kang ◽  
G. Huang ◽  
S. Yang
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Target Point ◽  
Point Cloud Data ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Gross Error ◽  
Error Elimination ◽  
K Nearest Neighbor Algorithm ◽  
Key Steps

Point cloud data has been one type of widely used data sources in the field of remote sensing. Key steps of point cloud data’s pro-processing focus on gross error elimination and quality control. Owing to the volume feature of point could data, existed gross error elimination methods need spend massive memory both in space and time. This paper employed a new method which based on Kd-tree algorithm to construct, k-nearest neighbor algorithm to search, settled appropriate threshold to determine with result turns out a judgement that whether target point is or not an outlier. Experimental results show that, our proposed algorithm will help to delete gross error in point cloud data and facilitate to decrease memory consumption, improve efficiency.

scholarly journals 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan Sbai
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
3D Point Cloud ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
New Approach ◽  
Entropy Estimation ◽  
3D Objects ◽  
Point Cloud Simplification

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

scholarly journals Efficient Shared Execution Processing of k-Nearest Neighbor Joins in Road Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Ju Cho
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Real Life ◽  
Road Networks ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Primitive Operation ◽  
Nested Loop

We investigate the k-nearest neighbor (kNN) join in road networks to determine the k-nearest neighbors (NNs) from a dataset S to every object in another dataset R. The kNN join is a primitive operation and is widely used in many data mining applications. However, it is an expensive operation because it combines the kNN query and the join operation, whereas most existing methods assume the use of the Euclidean distance metric. We alternatively consider the problem of processing kNN joins in road networks where the distance between two points is the length of the shortest path connecting them. We propose a shared execution-based approach called the group-nested loop (GNL) method that can efficiently evaluate kNN joins in road networks by exploiting grouping and shared execution. The GNL method can be easily implemented using existing kNN query algorithms. Extensive experiments using several real-life roadmaps confirm the superior performance and effectiveness of the proposed method in a wide range of problem settings.

scholarly journals PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3703
Author(s):  
Dongyang Cheng ◽  
Dangjun Zhao ◽  
Junchao Zhang ◽  
Caisheng Wei ◽  
Di Tian
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Noise Removal ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Optimization Strategy ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Run Speed

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

scholarly journals NormNet: Point-wise normal estimation network for three-dimensional point cloud data

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985753 ◽  
Author(s):  
Janghun Hyeon ◽  
Weonsuk Lee ◽  
Joo Hyung Kim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Three Dimensional ◽  
Local Feature ◽  
Data Sets ◽  
Point Cloud Data ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Normal Estimation ◽  
Local Feature Extraction

In this article, a point-wise normal estimation network for three-dimensional point cloud data called NormNet is proposed. We propose the multiscale K-nearest neighbor convolution module for strengthened local feature extraction. With the multiscale K-nearest neighbor convolution module and PointNet-like architecture, we achieved a hybrid of three features: a global feature, a semantic feature from the segmentation network, and a local feature from the multiscale K-nearest neighbor convolution module. Those features, by mutually supporting each other, not only increase the normal estimation performance but also enable the estimation to be robust under severe noise perturbations or point deficiencies. The performance was validated in three different data sets: Synthetic CAD data (ModelNet), RGB-D sensor-based real 3D PCD (S3DIS), and LiDAR sensor-based real 3D PCD that we built and shared.

scholarly journals Impact of Labeling Schemes on Dense Crowd Counting Using Convolutional Neural Networks with Multiscale Upsampling

2021 ◽  
pp. 2160012
Author(s):  
Greg Olmschenk ◽  
Xuan Wang ◽  
Hao Tang ◽  
Zhigang Zhu
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Nearest Neighbor ◽  
Safety Management ◽  
Superior Performance ◽  
K Nearest Neighbor ◽  
Crowd Counting ◽  
Crowd Density ◽  
Dense Crowd ◽  
The Impact

Gatherings of thousands to millions of people frequently occur for an enormous variety of educational, social, sporting, and political events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks may not be the most effective one. We propose an alternative inverse k-nearest neighbor (i[Formula: see text]NN) map mechanism that, even when used directly in existing state-of-the-art network structures, shows superior performance. We also provide new network architecture mechanisms that we demonstrate in our own MUD-i[Formula: see text]NN network architecture, which uses multi-scale drop-in replacement upsampling via transposed convolutions to take full advantage of the provided i[Formula: see text]NN labeling. This upsampling combined with the i[Formula: see text]NN maps further improves crowd counting accuracy. We further analyze several variations of the i[Formula: see text]NN labeling mechanism, which apply transformations on the [Formula: see text]NN measure before generating the map, in order to consider the impact of camera perspective views, image resolutions, and the changing rates of the mapping functions. To alleviate the effects of crowd density changes in each image, we also introduce an attenuation mechanism in the i[Formula: see text]NN mapping. Experimentally, we show that inverse square root [Formula: see text]NN map variation (iR[Formula: see text]NN) provides the best performance. Discussions are provided on computational complexity, label resolutions, the gains in mapping and upsampling, and details of critical cases such as various crowd counts, uneven crowd densities, and crowd occlusions.

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