scholarly journals A comparison of hole-filling methods in 3D

2016 ◽  
Vol 26 (4) ◽  
pp. 885-903 ◽  
Author(s):  
Emiliano Pérez ◽  
Santiago Salamanca ◽  
Pilar Merchán ◽  
Antonio Adán
Keyword(s):  
State Of The Art ◽  
3D Models ◽  
Hole Filling ◽  
Filling Process ◽  
Comprehensive Review ◽  
Common Framework ◽  
Pros And Cons ◽  
Filling State

Abstract This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework.

scholarly journals Robust Visibility Surface Determination in Object Space via Plücker Coordinates

2021 ◽  
Vol 7 (6) ◽  
pp. 96
Author(s):  
Alessandro Rossi ◽  
Marco Barbiero ◽  
Paolo Scremin ◽  
Ruggero Carli
Keyword(s):  
Finite Number ◽  
State Of The Art ◽  
Lossless Compression ◽  
3D Models ◽  
Optimal Result ◽  
Object Space ◽  
Plücker Coordinates ◽  
Determination Methods

Industrial 3D models are usually characterized by a large number of hidden faces and it is very important to simplify them. Visible-surface determination methods provide one of the most common solutions to the visibility problem. This study presents a robust technique to address the global visibility problem in object space that guarantees theoretical convergence to the optimal result. More specifically, we propose a strategy that, in a finite number of steps, determines if each face of the mesh is globally visible or not. The proposed method is based on the use of Plücker coordinates that allows it to provide an efficient way to determine the intersection between a ray and a triangle. This algorithm does not require pre-calculations such as estimating the normal at each face: this implies the resilience to normals orientation. We compared the performance of the proposed algorithm against a state-of-the-art technique. Results showed that our approach is more robust in terms of convergence to the maximum lossless compression.

scholarly journals The Understanding of Deep Learning: A Comprehensive Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ranjan Kumar Mishra ◽  
G. Y. Sandesh Reddy ◽  
Himanshu Pathak
Keyword(s):  
Deep Learning ◽  
Deep Neural Network ◽  
Computational Models ◽  
Review Paper ◽  
State Of The Art ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Levels Of Abstraction ◽  
Comprehensive Review ◽  
Computer Based

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

scholarly journals Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Composite Structures ◽  
Structural Damage ◽  
Structural Integrity ◽  
State Of The Art ◽  
Future Trends ◽  
Learning Models ◽  
Damage Diagnosis ◽  
Comprehensive Review ◽  
Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

scholarly journals Equipment Replacement Decision Making:Opportunities and Challenges

2013 ◽  
Author(s):  
Wei (David) Fan ◽  
Mason D. Gemar ◽  
Randy Machemehl
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
State Of The Art ◽  
Stochastic Dynamic ◽  
Comprehensive Review ◽  
Equipment Replacement ◽  
The Right ◽  
Art And State ◽  
Practice Literature ◽  
Solution Methodology

The primary function of equipment managers is to replace the right equipment at the right time and at the lowest overall cost. In this paper, the opportunities and challenges associated with equipment replacement optimization (ERO) are discussed in detail. First, a comprehensive review of the state-of-the art and state-of-the practice literature for the ERO problem is conducted. Second, a dynamic programming (DP) based optimization solution methodology is presented to solve the ERO problem. The Bellman’s formulation for the ERO deterministic (DDP) and stochastic dynamic programming (SDP) problems are discussed in detail. Finally, comprehensive ERO numerical results and implications are given.

scholarly journals A Review of Hydraulic Fracturing Simulation

2021 ◽  
Author(s):  
Bin Chen ◽  
Beatriz Ramos Barboza ◽  
Yanan Sun ◽  
Jie Bai ◽  
Hywel R Thomas ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
State Of The Art ◽  
Numerical Models ◽  
Gas Production ◽  
Physical Processes ◽  
Horizontal Drilling ◽  
Improve Treatment ◽  
Multi Stage ◽  
Pros And Cons ◽  
Treatment Designs

AbstractAlong with horizontal drilling techniques, multi-stage hydraulic fracturing has improved shale gas production significantly in past decades. In order to understand the mechanism of hydraulic fracturing and improve treatment designs, it is critical to conduct modelling to predict stimulated fractures. In this paper, related physical processes in hydraulic fracturing are firstly discussed and their effects on hydraulic fracturing processes are analysed. Then historical and state of the art numerical models for hydraulic fracturing are reviewed, to highlight the pros and cons of different numerical methods. Next, commercially available software for hydraulic fracturing design are discussed and key features are summarised. Finally, we draw conclusions from the previous discussions in relation to physics, method and applications and provide recommendations for further research.

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

Eine bibliometrische Analyse/PPC and machine learning A bibliometric analysis

2020 ◽  
Vol 110 (04) ◽  
pp. 220-225
Author(s):  
Matthias Schmidt ◽  
Janine Tatjana Maier ◽  
Mark Grothkopp
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Dynamic Environment ◽  
Production Planning And Control ◽  
Manufacturing Companies ◽  
Comprehensive Review ◽  
Planning And Control ◽  
Individual Task ◽  
The Individual ◽  
And Control

Produzierende Unternehmen stehen in einem dynamischen Umfeld vor der Herausforderung eine zunehmende Datenmenge effizienter zu verarbeiten. In diesem Zusammenhang werden häufig Ansätze des maschinellen Lernens (ML) diskutiert. Der Beitrag stellt eine umfassende Aufarbeitung des Stands der Forschung bezogen auf den Einsatz von ML-Ansätzen in der Produktionsplanung und -steuerung (PPS) bereit. Daraus lässt sich der Forschungsbedarf in den einzelnen Aufgabengebieten der PPS ableiten.   In a dynamic environment, manufacturing companies face the challenge of processing an increasing amount of data more efficiently. In this context, approaches of machine learning (ML) are often discussed. This paper provides a comprehensive review of the state of the art regarding the use of ML approaches in production planning and control (PPC). Based on this, the need for research in the individual task areas of PPC can be derived.

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