Academic literature on the topic 'Point cloud recovery'

Legacy database systems manage transactions under a concurrency control and a recovery protocol. The underlying operating system creates transaction execution platform and the database executes transactions concurrently. When the database system fails then the recovery manager applies “Undo” and/or “Redo” operations (depending upon the recovery protocol) to achieve the consistent state of the database. The recovery manager performs these set of operations as required by transaction execution platform. The availability of “Virtual” machines on cloud has given us an architecture that makes it possible to eliminate the effect of system or transaction failure by always taking the database to the next consistent state. We present a novel scheme of eliminating the effect of such failure by applying transaction “roll-forward” which resumes its execution from the point of failure. We refer to our system as AAP (Always Ahead Processing). Our work enables cloud providers to offer transactional HA-DBMS as an option that too with multiple data sources not necessarily relational.

3D recovery from multi-stereo and stereo images, as an important application of the image-based perspective geometry, serves many applications in computer vision, remote sensing, and Geomatics. In this chapter, the authors utilize the imaging geometry and present approaches that perform 3D reconstruction from cross-view images that are drastically different in their viewpoints. We introduce our project work that takes ground-view images and satellite images for full 3D recovery, which includes necessary methods in satellite and ground-based point cloud generation from images, 3D data co-registration, fusion, and mesh generation. We demonstrate our proposed framework on a dataset consisting of twelve satellite images and 150 k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.

Cloud computing provides a platform that enables users to utilize computation, storage, and other computing resources on-demand. As the number of running nodes in the cloud increases, the potential points of failure and the complexity of recovering from error states grows correspondingly. Using the traditional cloud administrative interface to manually detect and recover from errors is tedious, time-consuming, and error prone. This chapter presents an innovative approach to automate cloud error detection and recovery based on a run-time model that monitors and manages the running nodes in a cloud. When administrators identify and correct errors in the model, an inference engine is used to identify the specific state pattern in the model to which they were reacting, and to record their recovery actions. An error detection and recovery pattern can be generated from the inference and applied automatically whenever the same error occurs again.

There are many steps involved with securing a cloud system and its applications (SaaS) and developed ones in (PaaS). Security and privacy issues represent the biggest concerns to moving services to external clouds (Public). With cloud computing, data are stored and delivered across the Internet. The owner of the data does not have control or even know where their data are being stored. Additionally, in a multi-tenant environment, it may be very difficult for a cloud service provider to provide the level of isolation and associated guarantees that are possible with an environment dedicated to a single customer. Unfortunately, to develop a security algorithm that outlines and maps out the enforcement of a security policy and procedure can be a daunting task. A good security algorithm presents a strategy to counter the vulnerabilities in a cloud system. This chapter covers the complete overview, comparative analysis of security methods in Cloud Applications in STEM Education and the introduction of a new methodology that will enforce cloud computing security against breaches and intrusions. Much light will be shed on existing methodologies of security on servers used for cloud applications in STEM education and storage of data, and several methods will be presented in addition to the newly developed method of security in cloud-based servers, such as the MIST (Alkadi). Not only can cloud networks be used to gather sensitive information on multiple platforms, also there are needs to prevent common attacks through weak password recovery, retrieval, authentication, and hardening systems; otherwise hackers will spread cyber mayhem. Discussion of current security issues and algorithms in a real world will be presented. Different technologies are being created and in constant competition to meet the demands of users who are generally “busy”. The selling point of these technologies is the ability to address these demands without adding more to any workloads. One of the demands often discussed is that users want to have their digital information accessible from anywhere at any time. This information includes documents, audio libraries, and more. Users also demand the ability to manage, edit and update this information regardless of physical location. Somewhat recently, mobile devices such as laptops, tablets, and smartphones have provided these abilities. This is no small feat as vendors and providers have reduced the size of these devices to increase mobility. However, as the amount of personal information that users are wanting to access has grown exponentially, manipulation and storage of it require more capable devices. To meet increased demands, increasing the capabilities of mobile devices may be impractical. Making mobile devices more powerful without technological advancement would require that the device be larger and use more resources such as battery life and processing power to function properly. Storing all of a user's information on a mobile device that travels everywhere also adds vulnerability risks. The best technical solution to having a user's information accessible is some sort of online storage where there is the convenience to store, manipulate and retrieve data. This is one of the most practical applications for the concept of cloud computing in STEM education. As storage capabilities and Internet bandwidth has increased, so has the amount of personal data that users store online. And today, the average user has billions of bytes of data online. Access is everywhere and whenever is needed. As everyone started doing so, people want their data safe and secure to maintain their privacy. As the user base grew in size, the number of security issues of the personal data started to become increasingly important. As soon as someone's data are in the remote server, unwanted users or “hackers” can have many opportunities to compromise the data. As the online server needs to be up and running all the time, the only way to secure the cloud server is by using better passwords by every user. By the same token, the flaws in the password authentication and protection system can also help unwanted users to get their way to other people's personal data. Thus, the password authentication system should also be free from any loopholes and vulnerabilities.

There are many steps involved with securing a cloud system and its applications (SaaS) and developed ones in (PaaS). Security and privacy issues represent the biggest concerns to moving services to external clouds (Public). With cloud computing, data are stored and delivered across the Internet. The owner of the data does not have control or even know where their data are being stored. Additionally, in a multi-tenant environment, it may be very difficult for a cloud service provider to provide the level of isolation and associated guarantees that are possible with an environment dedicated to a single customer. Unfortunately, to develop a security algorithm that outlines and maps out the enforcement of a security policy and procedure can be a daunting task. A good security algorithm presents a strategy to counter the vulnerabilities in a cloud system. This chapter covers the complete overview, comparative analysis of security methods in Cloud Applications in STEM Education and the introduction of a new methodology that will enforce cloud computing security against breaches and intrusions. Much light will be shed on existing methodologies of security on servers used for cloud applications in STEM education and storage of data, and several methods will be presented in addition to the newly developed method of security in cloud-based servers, such as the MIST (Alkadi). Not only can cloud networks be used to gather sensitive information on multiple platforms, also there are needs to prevent common attacks through weak password recovery, retrieval, authentication, and hardening systems; otherwise hackers will spread cyber mayhem. Discussion of current security issues and algorithms in a real world will be presented. Different technologies are being created and in constant competition to meet the demands of users who are generally “busy”. The selling point of these technologies is the ability to address these demands without adding more to any workloads. One of the demands often discussed is that users want to have their digital information accessible from anywhere at any time. This information includes documents, audio libraries, and more. Users also demand the ability to manage, edit and update this information regardless of physical location. Somewhat recently, mobile devices such as laptops, tablets, and smartphones have provided these abilities. This is no small feat as vendors and providers have reduced the size of these devices to increase mobility. However, as the amount of personal information that users are wanting to access has grown exponentially, manipulation and storage of it require more capable devices. To meet increased demands, increasing the capabilities of mobile devices may be impractical. Making mobile devices more powerful without technological advancement would require that the device be larger and use more resources such as battery life and processing power to function properly. Storing all of a user's information on a mobile device that travels everywhere also adds vulnerability risks. The best technical solution to having a user's information accessible is some sort of online storage where there is the convenience to store, manipulate and retrieve data. This is one of the most practical applications for the concept of cloud computing in STEM education. As storage capabilities and Internet bandwidth has increased, so has the amount of personal data that users store online. And today, the average user has billions of bytes of data online. Access is everywhere and whenever is needed. As everyone started doing so, people want their data safe and secure to maintain their privacy. As the user base grew in size, the number of security issues of the personal data started to become increasingly important. As soon as someone's data are in the remote server, unwanted users or “hackers” can have many opportunities to compromise the data. As the online server needs to be up and running all the time, the only way to secure the cloud server is by using better passwords by every user. By the same token, the flaws in the password authentication and protection system can also help unwanted users to get their way to other people's personal data. Thus, the password authentication system should also be free from any loopholes and vulnerabilities.

Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time point cloud collection and processing since raw data usually requires large storage and computation. This paper addresses a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarded points in a case-agnostic manner (i.e., without additional storage for point relationships)? We propose a novel Locally Invertible Embedding (PointLIE) framework to unify the point cloud sampling and upsampling into one single framework through bi-directional learning. Specifically, PointLIE decouples the local geometric relationships between discarded points from the sampled points by progressively encoding the neighboring offsets to a latent variable. Once the latent variable is forced to obey a pre-defined distribution in the forward sampling path, the recovery can be achieved effectively through inverse operations. Taking the recover-pleasing sampled points and a latent embedding randomly drawn from the specified distribution as inputs, PointLIE can theoretically guarantee the fidelity of reconstruction and outperform state-of-the-arts quantitatively and qualitatively.

A robust method for surface fitting in 3D point cloud is presented as an application of the robust estimation of multiple in-lier structures algorithm [1]. The geometric primitives such as planes, spheres and cylinders are detected from the point samples in the noisy dataset, without regenerating surface normals or mesh. The inlier points of different surfaces are classified and segmented, with the tolerance of error for each surface estimated adaptively from the input data. From the segmented points, designers can interact with the geometric primitives conveniently. Direct modification of 3D point cloud and inverse design of solid model can be applied. Both synthetic and real point cloud datasets are tested for the use of the robust algorithm.

Abstract A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.

Abstract Surfactant performance is a function of its hydrophobic tail, and hydrophilic head in combination with crude oil composition, brine salinity, rock composition, and reservoir temperature. Specifically, for nonionic surfactants, temperature is a dominant variable due to the nature of the ethylene oxide (EO) groups in the hydrophilic head known as the cloud point temperature. This study aims to highlight the existence of temperature operating window for nonionic surfactants to optimize oil recovery during EOR applications in unconventional reservoirs. Two nonylphenol (NP) ethoxylated nonionic surfactants with different EO head groups were investigated in this study. A medium and light grade crude oil were utilized for this study. Core plugs from a carbonate-rich outcrop and a quartz-rich outcrop were used for imbibition experiments. Interfacial tension and contact angle measurements were performed to investigate the effect of temperature on the surfactant interaction in an oil/brine and oil/brine/rock system respectively. Finally, a series of spontaneous imbibition experiments was performed on three temperatures selected based on the cloud point of each surfactant in order to construct a temperature operating window for each surfactant. Both nonionic surfactants were observed to improve oil recovery from the two oil-wet oil/rock system tested in this study. The improvement was observed on both final recovery and rate of spontaneous imbibition. However, it was observed that each nonionic surfactant has its optimum temperature operating window relative to the cloud point of that surfactant. For both nonionic surfactants tested in this study, this window begins from the cloud point of the surfactant up to 25°F above the cloud point. Below this operating window, the surfactant showed subpar performance in increasing oil recovery. This behavior is caused by the thermodynamic equilibrium of the surfactant at this temperature which drives the molecule to be more soluble in the aqueous-phase as opposed to partitioning at the interface. Above the operating window, surfactant performance was also inferior. Although for this condition, the behavior is caused by the preference of the surfactant molecule to be in the oleic-phase rather than the aqueous-phase. One important conclusion is the surfactant achieved its optimum performance when it positions itself on the oil/water interface, and this configuration is achieved when the temperature of the system is in the operating window mentioned above. Additionally, it was also observed that the 25°F operating window varies based on the characteristic of the crude oil. A surfactant study is generally performed on a single basin, with a single crude oil on a single reservoir temperature or even on a proxy model at room temperature. This study aims to highlight the importance of applying the correct reservoir temperature when investigating nonionic surfactant behavior. Furthermore, this study aims to introduce a temperature operating window concept for nonionic surfactants. This work demonstrates that there is not a "one size fits all" surfactant design.

A 3D object can be recovered from scanned point data, which requires accurate estimating normal directions of the object surface from the cloud data. Many point cloud processing algorithms rely on the accurate normal as input to generate an accurate 3D surface model. The neighborhood of a data point in its smooth region can be well approximated by a plane. However, the neighborhood of a feature point employed for the normal estimation is isotropic which would enclose points belonging to different surface patches across the sharp feature. In this paper, isotropic neighborhoods are segmented to search anisotropic neighborhoods for the accurate normal estimation. Normals and candidate feature points are first estimated by the principal component analysis (PCA) method. Neighborhoods of the feature point are then mapped into a Gaussian image. A k-means clustering algorithm is then used for the Gaussian image to identify an anisotropic sub-neighborhood for the data point. The normal of the candidate feature point is finally estimated by the anisotropic neighborhood with the PCA method. The proposed method can accurately estimate normal directions while preserving sharp features of the object surface. Applications have demonstrated the effectiveness of the proposed method.

Abstract In complex industrial human-robot collaboration (HRC) environment, obstacles in the shared working space will occlude the operator, and the industrial robot will threaten the safety of the operator if it is unable to get the complete human spatial point cloud. This paper proposes a real-time human point cloud inpainting method based on the deep generative model. The method can recover the human point cloud occluded by obstacles in the shared working space to ensure the safety of the operator. The method proposed in this paper can be mainly divided into three parts: (i) real-time obstacles detection. This process can detect obstacle locations in real time and generate the image of obstacles. (ii) the application of the deep generative model algorithm. It is a complete convolutional neural network (CNN) structure and introduces advanced generative adversarial loss. The model can generate the missing depth data of operators at arbitrary position in the human depth image. (iii) spatial mapping of the depth image. The depth image will be mapped to point cloud by coordinate system conversion. The effectiveness of the method is verified by filling hole of the human point cloud occluded by obstacles in industrial HRC environment. The experiment results show that the proposed method can accurately generate the occluded human point cloud in real time and ensure the safety of the operator.