scholarly journals A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jilong Li ◽  
Jianlin Cheng
Keyword(s):  
Point Cloud ◽  
Sampling Method ◽  
Comparative Modeling ◽  
Template Protein

A New Surface Reconstruction Algorithm for Noisy 3D Unorganized Points

2012 ◽  
Vol 217-219 ◽  
pp. 1312-1317
Author(s):  
Jun Song
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Sampling Method ◽  
Reconstruction Algorithm ◽  
Improved Method ◽  
Feature Size ◽  
Data Set ◽  
Good Surface ◽  
Fuzzy C Means Clustering ◽  
Local Feature Size

This paper puts forward a new method of surface reconstruction. Power crust algorithm can reconstruct a good surface that is topological valid and be proved theoretically. But when the point cloud is noisy, the surface reconstructed is not good and its running time is long. This paper proposes a improved method of fuzzy c-means clustering to delete the noisy points and a non-uniformly sampling method to resample the input data set according to the local feature size before reconstruction. Experimental results show that the efficiency of the algorithm has been improved much more.

scholarly journals A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 202
Author(s):  
Yanfeng Gao ◽  
Cicao Ping ◽  
Ling Wang ◽  
Binrui Wang
Keyword(s):  
Point Cloud ◽  
Sampling Method ◽  
Steel Plate ◽  
Point Cloud Data ◽  
Gravity Center ◽  
Cloud Data ◽  
Point Cloud Simplification ◽  
Regional Gravity ◽  
The Given ◽  
Neighborhood Method

According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.

3D Remodeling of Molds Broken Area Based on Reverse Engineering

2013 ◽  
Vol 860-863 ◽  
pp. 2640-2643
Author(s):  
Jin Hua Li ◽  
De Qiang Zhang ◽  
Jian Li Zhang ◽  
Jie Cheng ◽  
Fang Ping Yao
Keyword(s):  
Reverse Engineering ◽  
Traditional Method ◽  
Point Cloud ◽  
Sampling Method ◽  
Surface Model ◽  
Point Cloud Data ◽  
Important Data ◽  
Engineering Technology ◽  
Cloud Data ◽  
Contact Measurement

The mold’s broken area is irregular, so it is difficult to model by traditional method. It puts forwards to remodel the mold’s broken area by Reverse Engineering technology. Non-contact measurement and contact measurement are both used to measure the broken mold. The pretreatment tasks are done including the point cloud data reduction Chord where the deviation sampling method is used. The surface model of the mold is remodeled and solidified. The broken area is extracted and solidified, and it provides the important data for the following repairing work.

scholarly journals INDOOR SCENE REGISTRATION BASED ON SIAMESE NETWORK AND POINTNET

2019 ◽  
Vol IV-2/W5 ◽  
pp. 307-312 ◽  
Author(s):  
Z. Zhang ◽  
C. Wen ◽  
Y. Chen ◽  
W. Li ◽  
C. You ◽  
...  
Keyword(s):  
Point Cloud ◽  
Sampling Method ◽  
Fine Tuning ◽  
Lidar Data ◽  
Registration Accuracy ◽  
Input Point ◽  
Siamese Network ◽  
Key Points ◽  
Feature Based ◽  
Robust To Noise

<p><strong>Abstract.</strong> This paper presents a deep learning feature-based method for registration of indoor mobile LiDAR data. Our method is to input point cloud directly, which is more robust to noise than traditional algorithms. The proposed method involves three steps. We first extract the key points by Harris3D algorithm and get their local patches by our sampling method. Second, a Siamese network is trained to describe the patches as local descriptors. Finally, we obtain the final matching pairs depends on the distance which is between two descriptors, and then solve the transformation matrix. The accuracy of registration is within 6&amp;thinsp;cm when the overlap is greater than 35%. In order to improve the registration accuracy, the ICP algorithm is used to fine-tuning the registration results. And the final registration accuracy is within 3.5&amp;thinsp;cm. The experiments show that our method applied to the registration of indoor mobile LiDAR data robustly and accurately.</p>

scholarly journals An Iterative Coarse-to-Fine Sub-Sampling Method for Density Reduction of Terrain Point Clouds

2019 ◽  
Vol 11 (8) ◽  
pp. 947 ◽  
Author(s):  
Lei Fan ◽  
Peter M. Atkinson
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Sampling Method ◽  
Computational Cost ◽  
Point Clouds ◽  
Free Access ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Density ◽  
Coarse To Fine

Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing.

An Empirical Analysis of the Obtrusiveness of and Participants' Compliance with the Electronically Activated Recorder (EAR)

2007 ◽  
Vol 23 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Matthias R. Mehl ◽  
Shannon E. Holleran
Keyword(s):  
Empirical Analysis ◽  
Sampling Method ◽  
Daily Life ◽  
Assessment Method ◽  
Data Confidentiality ◽  
Short Term ◽  
Naturally Occurring ◽  
Two Samples ◽  
Audio Recorder ◽  
Term Monitoring

Abstract. In this article, the authors provide an empirical analysis of the obtrusiveness of and participants' compliance with a relatively new psychological ambulatory assessment method, called the electronically activated recorder or EAR. The EAR is a modified portable audio-recorder that periodically records snippets of ambient sounds from participants' daily environments. In tracking moment-to-moment ambient sounds, the EAR yields an acoustic log of a person's day as it unfolds. As a naturalistic observation sampling method, it provides an observer's account of daily life and is optimized for the assessment of audible aspects of participants' naturally-occurring social behaviors and interactions. Measures of self-reported and behaviorally-assessed EAR obtrusiveness and compliance were analyzed in two samples. After an initial 2-h period of relative obtrusiveness, participants habituated to wearing the EAR and perceived it as fairly unobtrusive both in a short-term (2 days, N = 96) and a longer-term (10-11 days, N = 11) monitoring. Compliance with the method was high both during the short-term and longer-term monitoring. Somewhat reduced compliance was identified over the weekend; this effect appears to be specific to student populations. Important privacy and data confidentiality considerations around the EAR method are discussed.

Emotional Variability Predicts Tiredness in Daily Life

2016 ◽  
Vol 37 (3) ◽  
pp. 181-193 ◽  
Author(s):  
Aire Mill ◽  
Anu Realo ◽  
Jüri Allik
Keyword(s):  
Sampling Method ◽  
Daily Life ◽  
Emotional Experience ◽  
Experience Sampling ◽  
Experience Sampling Method ◽  
Building Blocks ◽  
Intraindividual Variability ◽  
Unique Role ◽  
Over Time

Abstract. Intraindividual variability, along with the more frequently studied between-person variability, has been argued to be one of the basic building blocks of emotional experience. The aim of the current study is to examine whether intraindividual variability in affect predicts tiredness in daily life. Intraindividual variability in affect was studied with the experience sampling method in a group of 110 participants (aged between 19 and 84 years) during 14 consecutive days on seven randomly determined occasions per day. The results suggest that affect variability is a stable construct over time and situations. Our findings also demonstrate that intraindividual variability in affect has a unique role in predicting increased levels of tiredness at the momentary level as well at the level of individuals.

Monitoring Daily Life Using Mobile Phones: The Experience Sampling Method

2006 ◽  
Author(s):  
Alessandra Preziosa ◽  
Marta Bassi ◽  
Daniela Villani ◽  
Andrea Gaggioli ◽  
Giuseppe Riva
Keyword(s):  
Mobile Phones ◽  
Sampling Method ◽  
Daily Life ◽  
Experience Sampling ◽  
Experience Sampling Method
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