Thematische Bibliographien / Point cloud recovery

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Auswahl der wissenschaftlichen Literatur zum Thema „Point cloud recovery“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Point cloud recovery"

Nie, Ziming, Qiao Wu, Chenlei Lv, et al. "SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6236–44. https://doi.org/10.1609/aaai.v39i6.32667.

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Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve upsampling by performing local interpolation between point clouds or in the feature space, then regressing the interpolated points to appropriate positions. By contrast, our proposed method treats point cloud upsampling as a global shape completion problem. Specifically, our method first divides the point cloud into multiple patches. Then a masking operation is applied to remove

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Zhou, Rong, Hua Sun, Kaisen Ma, et al. "Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points." Drones 7, no. 8 (2023): 524. http://dx.doi.org/10.3390/drones7080524.

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Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree at

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Zhang, Jianmin, Jiale Long, Zihao Du, Yi Ding, Yuyang Peng, and Jiangtao Xi. "Three-Dimensional Point Cloud-Filtering Method Based on Image Segmentation and Absolute Phase Recovery." Electronics 12, no. 12 (2023): 2749. http://dx.doi.org/10.3390/electronics12122749.

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In three-dimensional (3D) shape measurement based on fringe projection, various factors can degrade the quality of the point cloud. Existing point cloud filtering methods involve analyzing the geometric relationship between 3D space and point cloud, which poses challenges such as complex calculation and low efficiency. To improve the accuracy and speed of point cloud filtering, this paper proposes a new point cloud filtering method based on image segmentation and the absolute phase for the 3D imaging obtained by fringe projection. Firstly, a two-dimensional (2D) point cloud mapping image is es

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Hitesh Premshankar Rai, Pavan Ogeti, Narendra Sharad Fadnavis, Gireesh Bhaulal Patil, and Uday Krishna Padyana. "Disaster Recovery in Cloud Environments: Strategies for Business Continuity." International Journal for Research Publication and Seminar 10, no. 3 (2019): 111–21. http://dx.doi.org/10.36676/jrps.v10.i3.1460.

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The most important components of a firm are business continuity and disaster recovery planning, although they are often disregarded. Even before a crisis strikes, businesses need to have a well-organized strategy and documentation for business continuity and recovery after a disaster. A single cloud is characterised as a collection of servers housed in one or more data centres that are provided by a single supplier. Nonetheless, there are several reasons why switching from a single cloud to multiple clouds is sensible and crucial. For example, single cloud providers are still vulnerable to out

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Kresslein, Jacob, Payam Haghighi, Jaejong Park, Satchit Ramnath, Alok Sutradhar, and Jami J. Shah. "Automated cross-sectional shape recovery of 3D branching structures from point cloud." Journal of Computational Design and Engineering 5, no. 3 (2017): 368–78. http://dx.doi.org/10.1016/j.jcde.2017.11.010.

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Abstract Many applications rely on scanned data, which can come from a variety of sources: optical scanners, coordinate measuring machines, or medical imaging. We assume that the data input to these applications is an unorganized point cloud or mesh of vertices. The objective may be to find particular features (medical diagnostics or reverse engineering) or comparison to some reference geometry (e.g. dimensional metrology). This paper focuses on the feature fitting of a segmented point cloud, specifically for branched, organic structures or structural frames, and targets non-monolithic geometr

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Li, Lin, Huajun Wang, and Sen Wang. "Compressing and Recovering Short-Range MEMS-Based LiDAR Point Clouds Based on Adaptive Clustered Compressive Sensing and Application to 3D Rock Fragment Surface Point Clouds." Sensors 24, no. 17 (2024): 5695. http://dx.doi.org/10.3390/s24175695.

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Short-range MEMS-based (Micro Electronical Mechanical System) LiDAR provides precise point cloud datasets for rock fragment surfaces. However, there is more vibrational noise in MEMS-based LiDAR signals, which cannot guarantee that the reconstructed point cloud data are not distorted with a high compression ratio. Many studies have illustrated that wavelet-based clustered compressive sensing can improve reconstruction precision. The k-means clustering algorithm can be conveniently employed to obtain clusters; however, estimating a meaningful k value (i.e., the number of clusters) is challengin

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Su, Yunlian, Zhibo Chen, and Xiaojing Xue. "TreeDBH: Dual Enhancement Strategies for Tree Point Cloud Completion in Medium–Low Density UAV Data." Forests 16, no. 4 (2025): 667. https://doi.org/10.3390/f16040667.

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Medium–low density UAV point clouds often suffer from incomplete lower canopy structures and sparse distributions due to self-occlusion. While existing point cloud completion models achieve high metric accuracy, they inadequately address missing regions in trunks and lower canopy areas. To resolve these issues, this paper proposes a hierarchical random sampling strategy and a spatially constrained loss function. First, we dynamically stratify point clouds based on density distribution characteristics, employing hierarchical random sampling to preserve proportional representation of lower-level

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Barazzetti, Luigi. "Point cloud occlusion recovery with shallow feedforward neural networks." Advanced Engineering Informatics 38 (October 2018): 605–19. http://dx.doi.org/10.1016/j.aei.2018.09.007.

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Liang, Yan, Ye Hua Sheng, and Ka Zhang. "Method on 3D Dense Point Cloud Recovery of Geographical Scene." Advanced Materials Research 748 (August 2013): 619–23. http://dx.doi.org/10.4028/www.scientific.net/amr.748.619.

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The object of this research is to reconstruct 3D dense point cloud of geographical scene. With the technology and method of computer vision , first affine invariant features are extracted and matched, then cameras parameters and 3D dense point cloud are recovered and united under geographical reference. The experimental results show that this method with low cost and high precision of centimeters can satisfy the requirements of measurement, modeling and virtual reality.

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Yu, Fan, and Xue Fan. "3D Reconstruction System Based on Multi Sensor." International Journal of Advanced Network, Monitoring and Controls 7, no. 2 (2022): 58–66. http://dx.doi.org/10.2478/ijanmc-2022-0016.

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Abstract In the 3d dense map construction system of indoor scene by mobile robot, the existing single sensor method cannot improve the positioning accuracy and reconstruction accuracy of robot, as well as the requirement of rapidity. Therefore, it is applied to THE ORB-SLAM with three parallel threads of track tracking, map reconstruction and loopback detection. Through depth camera pose to splice point cloud of building three-dimensional dense point cloud, in the 3 d reconstruction, a computer can not rely on GPU parallel computing, using only the CPU recovery environment three-dimensional de

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Dissertationen zum Thema "Point cloud recovery"

Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.

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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The fram

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HermawanSutanto and 陳忠胜. "Recovery of Nonionic Surfactant after Cloud Point Extraction of Polycyclic Aromatic Hydrocarbons." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/61446516836050054149.

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碩士<br>國立成功大學<br>化學工程學系碩博士班<br>98<br>Cloud point extraction (CPE) has been applied successfully to remove the 9 compounds of polycyclic aromatic hydrocarbons (PAHs) by using nonionic surfactant Tergitol 15-S-7 as separating agent. Possibly, the CPE method may be applied in treating wastewater containing PAHs pollutants. In Addition, in order to make the process more economical and efficient, the surfactant in the surfactant rich phase should be recycled and reused. Solvent extraction and adsorption using activated carbon were used to separate the surfactant rich phase into surfactant and PAHs.

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Buchteile zum Thema "Point cloud recovery"

Huntley, David, Drew Rotheram-Clarke, Roger MacLeod, Robert Cocking, Jamel Joseph, and Philip LeSueur. "Landslide Monitoring with RADARSAT Constellation Mission InSAR, RPAS-Derived Point-Clouds and RTK-GNSS Time-Series in the Thompson River Valley, British Columbia, Canada." In Progress in Landslide Research and Technology, Volume 2 Issue 1, 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39012-8_19.

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AbstractIn this paper, we evaluate the effectiveness of four land-deformation measurement techniques for monitoring slow-moving landslides along a high-risk section of the national railway corridor traversing the Thompson River valley, British Columbia, Canada. The geomorphically active North Slide acts as an ideal field laboratory for testing and evaluating novel monitoring techniques and methods. We compare differential processing of Structure from Motion (SfM) products such as point-cloud elevation models and orthophotos derived from Remotely Piloted Aircraft Systems (RPAS), along with sate

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Gordon, Matthew, Luise von Zimmerman, Oushesh Haradhun, Dominik Campanella, Milena Bräutigam, and Catherine De Wolf. "Digitising Building Materials for Reuse with Reality Capture and Scan-to-BIM Technologies." In Circular Economy and Sustainability. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39675-5_3.

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AbstractEffective building component reuse requires specific information about recoverable components. However, 85% of the European building stock predates the building information modelling (BIM) technology that stores and links such information. Digitisation technologies can be used to recover this information. Scanning and scan-to-BIM technologies such as LiDAR and photogrammetry enable us to capture and analyse large amounts of raw geometric data as point clouds to create digital records or BIM models of existing buildings. These digital representations can be used by building owners, insp

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Jaiswal, Chetan, and Vijay Kumar. "Highly Available Fault-Tolerant Cloud Database Services." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0153-4.ch005.

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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 po

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Moraes Kátia Bottega, Martins Fabiana Zerbieri, de Camargo Maximiliano Dutra, Vieira Débora Feijó, Magalhães Ana Maria Muller, and Silveira Denise Tolfo. "Nursing Activities Score: Cloud Computerized Structure." In Studies in Health Technology and Informatics. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-658-3-836.

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This study objective to describe the cloud Nursing Activities Score implementation process in the Intensive Care Unit of the Post-Anesthesia Recovery Room. It is a case study. The tools used were the Google applications with high productivity interconnecting the topic knowledge on behalf of the nursing professionals and information technology professionals. As partial results, it was determined that the average nursing staff workload in the ICU/PARR during the first 24 hours, according to the score on the scale, was 91.75 &plusmn; 18.2. Each point of NAS is converted into 14.4 minutes, whi

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Qin, Rongjun, Shuang Song, Xiao Ling, and Mostafa Elhashash. "3D Reconstruction through Fusion of Cross-View Images." In Recent Advances in Image Restoration with Applications to Real World Problems. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93099.

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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-regist

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Oliinyk, Danyila, and Danyil Nizhnyi. "Certain aspects of the post-war recovery of Ukraine's digital infrastructure." In The development of innovations and financial technology in the digital economy. Scientific Center of Innovative Research, 2023. http://dx.doi.org/10.36690/diftde-2023-42-61.

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The policy in information and communication technologies serves as a basis for the digital transformation of the economy and society. It focuses on measures related to information technologies, communication networks, and services, including technological and security aspects of domestic digital infrastructure development. The policy is in the context of the development of trans-European infrastructure. The digital transition is a central point of the new strategy, which involves an international digital partnership to implement the EU4Digital Initiative in Ukraine. The strategy aims to focus

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Sun, Yu, Jules White, Jeff Gray, and Aniruddha Gokhale. "Model-Driven Automated Error Recovery in Cloud Computing." In Grid and Cloud Computing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0879-5.ch308.

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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 cl

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Alkadi, Ihssan. "Assessing Security with Regard to Cloud Applications in STEM Education." In Advances in Educational Technologies and Instructional Design. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9924-3.ch017.

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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

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Alkadi, Ihssan. "Assessing Security With Regard to Cloud Applications in STEM Education." In Cyber Security and Threats. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5634-3.ch014.

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Arora, Rajeev Kumar, Anoop Kumar, Arpita Soni, and Aniruddh Tiwari. "AI-Driven Self-Healing Cloud Systems: Enhancing Reliability and Reducing Downtime through Event-Driven Automation." In Applied Intelligence and Computing. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-955020-9-7-28.

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The goal of this study is to create and carry out a self-healing cloud system by combining an event-driven automation framework depending on the if-this-then-that principle for managing incidents and recovery. A recovery engine with Artificial Intelligence (AI)-based decision-making approaches is presented—which chooses the best remedial actions from a pre-established catalogue in order to maximise system reliability and minimise downtime. The system is tested on an OpenStack-based video on demand service—where multiple issues are replicated in order to assess the efficaciousness of various re

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Konferenzberichte zum Thema "Point cloud recovery"

Zhao, Weibing, Xu Yan, Jiantao Gao, et al. "PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/186.

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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 l

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Yang, Xiang, Peter Meer, and Hae Chang Gea. "Robust Recovery of 3D Geometric Primitives From Point Cloud." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67564.

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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

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Jang, Hojun, Minkwan Kim, Jinseok Bae, and Young Min Kim. "Dynamic Mesh Recovery from Partial Point Cloud Sequence." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01384.

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Hosseinyalamdary, Siavash, and Alper Yilmaz. "Surface Recovery: Fusion of Image and Point Cloud." In 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2015. http://dx.doi.org/10.1109/iccvw.2015.32.

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Deng, Jianjun, Jianan Lu, Hua Fan, et al. "TimeCloth: Fast Point-in-Time Database Recovery in The Cloud." In SIGMOD/PODS '24: International Conference on Management of Data. ACM, 2024. http://dx.doi.org/10.1145/3626246.3653382.

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Sun, Yunda, and Lin Zhang. "MaGo-I2P: Image-to-Point Cloud Registration with Mamba and Geometry Recovery." In ICMR '25: International Conference on Multimedia Retrieval. ACM, 2025. https://doi.org/10.1145/3731715.3733379.

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Long, Jiale, Zihao Du, Jianmin Zhang, and Yi Ding. "3D point cloud denoising method based on K nearest neighbor and absolute phase recovery." In Third International Computing Imaging Conference (CITA 2023), edited by Xiaopeng Shao. SPIE, 2023. http://dx.doi.org/10.1117/12.2688747.

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Alkhateeb, Mojahed, Jeremy L. Rickli, and Nicholas J. Christoforou. "Error Propagation in Digital Additive Remanufacturing Process Planning." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-3009.

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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

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Torabi, Farshid, and Asghar Gandomkar. "The Effect of Small Molecule Gas-Soluble Polymers on Minimum Miscibility Pressure During CO2 Injection." In SPE Improved Oil Recovery Conference. SPE, 2024. http://dx.doi.org/10.2118/218156-ms.

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Abstract The minimum miscibility pressure is the main technical challenges during CO2 flooding. Therefore, the purpose of this work is to explore the effect of small molecule CO2-soluble polymers including PDMS, PFA, P-1-D, and PVEE on minimum miscibility pressure during gas flooding. The dilute concentrations of polymers (1000 to 50000 ppm) were considered to dissolve in CO2 at 60 and 80 °C. According to the cloud point pressure results, the small molecule CO2-soluble polymers dissolved in carbon dioxide at pressures consistent with gas-based EOR methods (less than 2500 psi) at both 60 and 80

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Wang, Yinchu, Haijiang Zhu, and Guanghui Wang. "PST-Net: Point Cloud Completion Network Based on Local Geometric Feature Reuse and Neighboring Recovery with Taylor Approximation." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191922.

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