Relevant bibliographies by topics / Synthetic LiDAR data generation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Synthetic LiDAR data generation'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Synthetic LiDAR data generation"

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Kim, Kana, Sangjun Lee, Vijay Kakani, Xingyou Li, and Hakil Kim. "Point Cloud Wall Projection for Realistic Road Data Augmentation." Sensors 24, no. 24 (2024): 8144. https://doi.org/10.3390/s24248144.

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Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task. Although there are a few state-of-the-art techniques to generate points from synthetic objects using LiDAR point clouds, limitations such as the need for intense compute power still persist i
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Wang, Fei, Yan Zhuang, Hong Gu, and Huosheng Hu. "Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis." IEEE Transactions on Instrumentation and Measurement 68, no. 7 (2019): 2671–73. http://dx.doi.org/10.1109/tim.2019.2906416.

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Toro, Javier Villena, Lars Bolin, Jacob Eriksson, and Anton Wiberg. "Towards digital representations for brownfield factories using synthetic data generation and 3D object detection." Proceedings of the Design Society 4 (May 2024): 2297–306. http://dx.doi.org/10.1017/pds.2024.232.

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AbstractThis study emphasizes the importance of automatic synthetic data generation in data-driven applications, especially in the development of a 3D computer vision system for engineering contexts such as brownfield factory projects, where no data is readily available. Key points: (1) A successful integration of a synthetic data generator with the S3DIS dataset, leading to a significant enhancement in object detection of previous classes and enabling recognition of new ones; (2) A proposal for a CAD-based configurator for efficient and customizable scene reconstruction from LiDAR scanner poi
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Gusmão, Guilherme Ferreira, Carlos Roberto Hall Barbosa, and Alberto Barbosa Raposo. "Development and Validation of LiDAR Sensor Simulators Based on Parallel Raycasting." Sensors 20, no. 24 (2020): 7186. http://dx.doi.org/10.3390/s20247186.

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Three-dimensional (3D) imaging technologies have been increasingly explored in academia and the industrial sector, especially the ones yielding point clouds. However, obtaining these data can still be expensive and time-consuming, reducing the efficiency of procedures dependent on large datasets, such as the generation of data for machine learning training, forest canopy calculation, and subsea survey. A trending solution is developing simulators for imaging systems, performing the virtual scanning of the digital world, and generating synthetic point clouds from the targets. This work presents
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Schuster, Alexander, Raphael Hagmanns, Iman Sonji, et al. "Synthetic data generation for the continuous development and testing of autonomous construction machinery." at - Automatisierungstechnik 71, no. 11 (2023): 953–68. http://dx.doi.org/10.1515/auto-2023-0026.

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Abstract The development and testing of autonomous systems require sufficient meaningful data. However, generating suitable scenario data is a challenging task. In particular, it raises the question of how to narrow down what kind of data should be considered meaningful. Autonomous systems are characterized by their ability to cope with uncertain situations, i.e. complex and unknown environmental conditions. Due to this openness, the definition of training and test scenarios cannot be easily specified. Not all relevant influences can be sufficiently specified with requirements in advance, espe
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Soto Sagredo, Esperanza, Ásta Hannesdóttir, Jennifer M. Rinker, and Michael Courtney. "Reconstructing turbulent wind-fields using inverse-distance-weighting interpolation and measurements from a pulsed mounted-hub lidar." Journal of Physics: Conference Series 2745, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2745/1/012017.

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Abstract This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The
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Zhang, Junge, Feihu Zhang, Shaochen Kuang, and Li Zhang. "NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7178–86. http://dx.doi.org/10.1609/aaai.v38i7.28546.

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Labelling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for novel view synthesis using implicit reconstruction of 3D scenes. Inspired by this, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages real-world information to generate realistic LIDAR point clouds. Different from existing LiDAR simulators, we use real images and point cloud data
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Chitnis, S. A., Z. Huang, and K. Khoshelham. "GENERATING SYNTHETIC 3D POINT SEGMENTS FOR IMPROVED CLASSIFICATION OF MOBILE LIDAR POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 139–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-139-2021.

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Abstract. Mobile lidar point clouds are commonly used for 3d mapping of road environments as they provide a rich, highly detailed geometric representation of objects on and around the road. However, raw lidar point clouds lack semantic information about the type of objects, which is necessary for various applications. Existing methods for the classification of objects in mobile lidar data, including state of the art deep learning methods, achieve relatively low accuracies, and a primary reason for this under-performance is the inadequacy of available 3d training samples to sufficiently train d
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Donovan, David P., Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff. "The generation of EarthCARE L1 test data sets using atmospheric model data sets." Atmospheric Measurement Techniques 16, no. 21 (2023): 5327–56. http://dx.doi.org/10.5194/amt-16-5327-2023.

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Abstract. The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation process study mission embarking a high spectral resolution lidar, a cloud profiling radar, a multi-spectral imager, and a three-view broadband radiometer. An important aspect of the EarthCARE mission is its focus on instrument synergy. Many L2 products are the result of L1 inputs from one or more instruments. Since no existing complete observational proxy data sets comprised of co-located and co-temporal “EarthCARE-like” data exists, it has been necessary to create synthe
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Goo, June Moh, Zichao Zeng, and Jan Boehm. "Zero-Shot Detection of Buildings in Mobile LiDAR using Language Vision Model." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 107–13. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-107-2024.

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Abstract. Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts to apply LVMs to three-dimensional (3D) data. While LVMs are efficient and effective in addressing various downstream 2D vision tasks without training, they face significant challenges when it comes to point clouds, a representative format for representing 3D data. It is more difficult to extract features from 3D data and there are challenges due to large data sizes and the cost of the collection and label
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Dissertations / Theses on the topic "Synthetic LiDAR data generation"

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Reje, Niklas. "Synthetic Data Generation for Anonymization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276239.

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Because of regulations but also from a need to find willing participants for surveys, any released data needs to have some sort of privacy preservation. Privacy preservation, however, always requires some sort of reduction of the utility of the data, how much can vary with the method. Synthetic data generation seeks to be a privacy preserving alternative that keeps the privacy of the participants by generating new records that do not correspond to any real individuals/organizations but still preserve relationships and information within the original dataset. For a method to see wide adoption h
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Lee, Hyun Seung. "A HYBRID MODEL FOR DTM GENERATION FROM LIDAR DATA." MSSTATE, 2004. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022004-053808/.

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This dissertation introduces an innovative technique to extract ground elevation models using small-footprint LIDAR data. This technique consists of a preprocessing step, ground modeling, and interpolation. In the preprocessing step, much of the non-terrain points are eliminated using a histogram-based clustering technique. Then, in the ground modeling stage, the information such as elevation and slope between nearest neighbor points is extracted. This step corresponds to an outlier detection process. In this stage, residuals and gradient indices for elevation and slope, are introduced. These
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Edhammer, Jens. "Rigid Body Physics for Synthetic Data Generation." Thesis, Linköpings universitet, Informationskodning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129808.

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For synthetic data generation with concave collision objects, two physics simu- lations techniques are investigated; convex decomposition of mesh models for globally concave collision results, used with the physics simulation library Bullet, and a GPU implemented rigid body solver using spherical decomposition and impulse based physics with a spatial sorting-based collision detection. Using the GPU solution for rigid body physics suggested in the thesis scenes con- taining large amounts of bodies results in a rigid body simulation up to 2 times faster than Bullet 2.83.
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Garcia, Torres Douglas. "Generation of Synthetic Data with Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254366.

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The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when the data is sensitive to use, or simply when it is hard to get access to the real data. Traditional methods of synthetic data generation use techniques that do not intend to replicate important statistical properties of the original data. Properties such as the distribution, the patterns or the correlation between variables, are often omitted. Moreover, most of the existing tools and approaches requi
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Fowler, Lee Everett. "A Virtual pilot algorithm for synthetic HUMS data generation." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54473.

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Regime recognition is an important tool used in creation of usage spectra and fatigue loads analysis. While a variety of regime recognition algorithms have been developed and deployed to date, verification and validation (V&V) of such algorithms is still a labor intensive process that is largely subjective. The current V&V process for regime recognition codes involves a comparison of scripted flight test data to regime recognition algorithm outputs. This is problematic because scripted flight test data is expensive to obtain, may not accurately match the maneuver script, and is often used t
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Torfi, Amirsina. "Privacy-Preserving Synthetic Medical Data Generation with Deep Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99856.

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Deep learning models demonstrated good performance in various domains such as ComputerVision and Natural Language Processing. However, the utilization of data-driven methods in healthcare raises privacy concerns, which creates limitations for collaborative research. A remedy to this problem is to generate and employ synthetic data to address privacy concerns. Existing methods for artificial data generation suffer from different limitations, such as being bound to particular use cases. Furthermore, their generalizability to real-world problems is controversial regarding the uncertainties in def
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Hammond, Patrick Douglas. "Deep Synthetic Noise Generation for RGB-D Data Augmentation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7516.

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Considerable effort has been devoted to finding reliable methods of correcting noisy RGB-D images captured with unreliable depth-sensing technologies. Supervised neural networks have been shown to be capable of RGB-D image correction, but require copious amounts of carefully-corrected ground-truth data to train effectively. Data collection is laborious and time-intensive, especially for large datasets, and generation of ground-truth training data tends to be subject to human error. It might be possible to train an effective method on a relatively smaller dataset using synthetically damaged dep
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Montanez, Andrew M. Eng Massachusetts Institute of Technology. "SDV : an open source library for synthetic data generation." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121631.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 105).<br>In this thesis, I designed three open source Python libraries with the intention of creating a robust system that can accurately generate synthetic data. The goals of this thesis were to separate the different componen
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Choudhury, Ananya. "WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72256.

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Human behavior is a key factor influencing the spread of infectious diseases. Individuals adapt their daily routine and typical behavior during the course of an epidemic -- the adaptation is based on their perception of risk of contracting the disease and its impact. As a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data collection methods are not well su
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Ali, Arslan Mehmet. "Generation and Bioinformatic Analysis of Synthetic Ago HITS-CLIP Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-204891.

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Micro-RNAs (miRNAs) have been discovered to regulate messenger RNA (mRNA) translation and degradation. Various recent studies have been focused on miRNA target prediction, in order to get a better understanding of the rules and nature of miRNA regulation over mRNAs. In this project we aim to create a software module to identify miRNA target sites on mRNAs. As basis to this project, we refer to a study that identified a platform for miRNA-mRNA interaction in protein-RNA complexes in mouse brain (AGO HITS-CLIP study). We propose a probabilistic model of the data from this study, and generate syn
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Books on the topic "Synthetic LiDAR data generation"

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Emam, Khaled El, Richard Hoptroff, and Lucy Mosquera. Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. O'Reilly Media, Incorporated, 2020.

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Wich, Serge A., and Lian Pin Koh. Sensors. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198787617.003.0003.

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The number of sensors that can be fitted and/or have been specifically designed to be fitted to drones is expanding rapidly. This chapter provides an overview of the various types of sensors used on drones for conservation research and monitoring, including RGB cameras, multispectral and hyperspectral cameras, and thermal imaging cameras. Increasing miniaturization means LiDAR and synthetic aperture radar (SAR) sensors can now also be fitted to drones, and they are also discussed briefly, as are a number of other types (e.g. acoustic and gas sensors) now being developed. Because most conservat
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Book chapters on the topic "Synthetic LiDAR data generation"

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Eggert, Mathias, Maximilian Schade, Florian Bröhl, and Alexander Moriz. "Generating Synthetic LiDAR Point Cloud Data for Object Detection Using the Unreal Game Engine." In Design Science Research for a Resilient Future. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61175-9_20.

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Cajas, Dany. "Generation of Synthetic Data." In Advanced Portfolio Optimization. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84304-4_14.

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Fosci, Paolo, Javier Nieves, Giuseppe Psaila, and Pablo Garcia Bringas. "Bayesian Generation of Synthetic Data." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75013-7_18.

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Gürsakal, Necmi, Sadullah Çelik, and Esma Birişçi. "Synthetic Data Generation with Python." In Synthetic Data for Deep Learning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8587-9_5.

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Gürsakal, Necmi, Sadullah Çelik, and Esma Birişçi. "Synthetic Data Generation with R." In Synthetic Data for Deep Learning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8587-9_4.

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Snoke, Joshua, and Satkartar K. Kinney. "Methods for Synthetic Data Generation." In Handbook of Sharing Confidential Data. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003185284-14.

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Kumar, Amala Rashmi, G. K. Ravikumar, G. Sathisha, and H. V. Chethan. "Exploring Synthetic Data Generation Methods." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4430-8_36.

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Behl, Harkirat Singh, Atilim Güneş Baydin, Ran Gal, Philip H. S. Torr, and Vibhav Vineet. "AutoSimulate: (Quickly) Learning Synthetic Data Generation." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_16.

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Lundin, Emilie, Håkan Kvarnström, and Erland Jonsson. "A Synthetic Fraud Data Generation Methodology." In Information and Communications Security. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36159-6_23.

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Chakradhar, Bhogineni Sankha, Mugil Vijey, and Suraj Tunk. "Synthetic Data Generation for Marketing Insights." In Predictive Analytics and Generative AI for Data-Driven Marketing Strategies. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003472544-17.

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Conference papers on the topic "Synthetic LiDAR data generation"

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Scholes, Stirling, German Mora-Martin, Istvan Gyongy, Gerald Buller, and Jonathan Leach. "Generation of photo-realistic SPAD-lidar data." In Electro-optical and Infrared Systems: Technology and Applications XXI, edited by Duncan L. Hickman, Helge Bürsing, Philip J. Soan, and Ove Steinvall. SPIE, 2024. http://dx.doi.org/10.1117/12.3031447.

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Aly, Karim, and Alexei Sharpanskykh. "Synthetic Flight Data Generation Using Generative Models." In 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2025. https://doi.org/10.1109/icns65417.2025.10976960.

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Utley, Jeffrey W., Gregery T. Buzzard, Charles A. Bouman, and Matthew R. Kemnetz. "Data driven synthetic wavefront generation for boundary layer data." In Unconventional Imaging, Sensing, and Adaptive Optics 2024, edited by Santasri R. Bose-Pillai, Jean J. Dolne, and Matthew Kalensky. SPIE, 2024. http://dx.doi.org/10.1117/12.3027740.

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Suganthi, M., K. Tamizh Selvan, A. Risen Bright, S. Loganathan, and R. Sathya. "Secure Data Masking Through Synthetic Data Generation using ML." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11042182.

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Channegowda, Janamejaya, Vageesh Maiya, and Chaitanya Lingaraj. "An Interpolation based Synthetic Battery Data Generation Technique." In 2024 IEEE International Communications Energy Conference (INTELEC). IEEE, 2024. http://dx.doi.org/10.1109/intelec60315.2024.10679036.

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Tenison, Irene, Ashley Chen, Navpreet Singh, Omar Dahleh, Eliott Zemour, and Lalana Kagal. "Private Synthetic Data Generation for Mixed Type Datasets." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825249.

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Goyal, Mandeep, and Qusay H. Mahmoud. "An LLM-Based Framework for Synthetic Data Generation." In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025. https://doi.org/10.1109/ccwc62904.2025.10903878.

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Mathur, Arpit, and Kartik Shah. "TSDCG: Tabular Synthetic Data with Code Generation LLMs." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069449.

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Kang, Ha Ye Jin, Min Sam Ko, and Kwang Sun Ryu. "Generation of Synthetic Data for Sharing and Utilization in Healthcare Data." In 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC). IEEE, 2024. https://doi.org/10.1109/dsc63484.2024.00116.

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Saisho, Osamu, Takayuki Miura, Kazuki Iwahana, Masanobu Kii, and Rina Okada. "Efficient Privacy-Preserving Data Annotation via Active PrivBayes Synthetic Data Generation." In 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2025. https://doi.org/10.1109/percomworkshops65533.2025.00159.

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Reports on the topic "Synthetic LiDAR data generation"

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Goncalves, Andre R., Ana Paula Sales, Priyadip Ray, and Braden Soper. NCI Pilot 3 - Synthetic Data Generation Report. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1430997.

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Aldrich, C. H. III. [Generation of a synthetic seismic data base]. Final report. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/215720.

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Kargl, Steven G. Generation of Synthetic SAS Data for Targets Near the Seafloor: Propagation Component. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada542126.

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Kargl, Steven G. Generation of Synthetic SAS Data for Targets near the Seafloor: Propagation Component. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574940.

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Kargl, Steven G. Generation of Synthetic SAS Data for Targets Near the Seafloor: Propagation Component. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada591721.

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Purohit, Sumit, Patrick Mackey, Joseph Cottam, Madelyn Dunning, and George Chin. Synthetic Data and Graph Generation for Modeling Adversarial Activity – Final Project Report. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1871012.

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Trott, Kevin. Analysis of Digital Topographic Data Issues In Support of Synthetic Environment Terrain Data Base Generation. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada351879.

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Powers, Sarah S., and Joshua Lothian. Synthetic graph generation for data-intensive HPC benchmarking: Scalability, analysis and real-world application. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1214496.

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Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40042.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and pro
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Maddipatla, Yashwanth. VR Co-Lab: A Virtual Reality Platform for Human-Robot Disassembly Training and Synthetic Data Generation. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-95.

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