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Thematische Bibliographien / 3D model-Driven reconstruction / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „3D model-Driven reconstruction“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Meng, Ge, Jingyan Tu, Jingjia Huang, et al. "Sp3ctralMamba: Physics-Driven Joint State Space Model for Hyperspectral Image Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6108–16. https://doi.org/10.1609/aaai.v39i6.32653.

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Hyperspectral image (HSI) reconstruction aims to restore the original 3D HSIs from the 2D hyperspectral snapshot compressive images (SCIs). The key to high-fidelity HSI reconstruction lies in designing refined spatial and spectral attention mechanisms, which are crucial for generating fine-grained representations of HSI based on the limited spatial and spectral information available in SCI. Recently, Mamba has demonstrated remarkable performance and efficiency in modeling spatial correlations. Its implicit attention mechanism generates three orders of magnitude more attention matrices than tra

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Huang, Wei, San Jiang, and Wanshou Jiang. "A Model-Driven Method for Pylon Reconstruction from Oblique UAV Images." Sensors 20, no. 3 (2020): 824. http://dx.doi.org/10.3390/s20030824.

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Pylons play an important role in the safe operation of power transmission grids. Directly reconstructing pylons from UAV images is still a great challenge due to problems of weak texture, hollow-carved structure, and self-occlusion. This paper presents an automatic model-driven method for pylon reconstruction from oblique UAV images. The pylons are reconstructed with the aid of the 3D parametric model library, which is represented by connected key points based on symmetry and coplanarity. First, an efficient pylon detection method is applied to detect the pylons in the proposed region, which a

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Troccaz, J., and P. Cinquin. "Model Driven Therapy." Methods of Information in Medicine 42, no. 02 (2003): 169–76. http://dx.doi.org/10.1055/s-0038-1634329.

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Summary Objectives: Taking into account a priori knowledge is a key issue to meet the medical, scientific and industrial challenges of the progresses of Minimally Invasive Surgery. We propose an overview of these challenges. Methods: Models play a major role in representing the relevant knowledge to plan and realize complex medical and surgical interventions. We analyze the three basic steps of Perception, Decision and Action, and illustrate by some instances how models may be integrated in these steps. Results: We propose a selection of the results obtained in Model Driven Therapy. These resu

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Nguyen, Duc-Phong, Tan-Nhu Nguyen, Stéphanie Dakpé, Marie-Christine Ho Ba Ho Ba Tho, and Tien-Tuan Dao. "Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients." Bioengineering 9, no. 11 (2022): 619. http://dx.doi.org/10.3390/bioengineering9110619.

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The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape mod

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Pistellato, Mara, Filippo Bergamasco, Andrea Torsello, et al. "A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction." Remote Sensing 13, no. 18 (2021): 3780. http://dx.doi.org/10.3390/rs13183780.

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One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to ite

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Liu, Yilin, Liqiang Lin, Yue Hu, et al. "Learning Reconstructability for Drone Aerial Path Planning." ACM Transactions on Graphics 41, no. 6 (2022): 1–17. http://dx.doi.org/10.1145/3550454.3555433.

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We introduce the first learning-based reconstructability predictor to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that explicitly predicts how well a 3D urban scene will be reconstructed from a set of viewpoints. To make such a model trainable and simultaneously applicable to drone path planning, we simulate the proxy-based 3D scene reconstruction during training to set up the prediction. Specifically, the neural network we design is trained to predict the scene reconstr

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Li, Jinwen, Guangli Ren, Youmei Pan, et al. "Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching." Remote Sensing 17, no. 14 (2025): 2390. https://doi.org/10.3390/rs17142390.

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Advancements in remote sensing technology have remarkably enhanced the 3D Earth surface reconstruction, which is pivotal for applications such as disaster relief, emergency management, and urban planning, etc. Although satellite imagery offers a cost-effective and extensive coverage solution for 3D reconstruction, the quality of the resulted digital surface model (DSM) heavily relies on the choice of stereo image pairs. However, current approaches of stereo Earth observation still employ a post-acquisition manner without sophisticated planning in advance, causing inefficiencies and low reconst

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Hou, Yaohui, Jianwen Song, and Lijun Wang. "P‐2.27: Application of 3D reconstruction technology in VR industry." SID Symposium Digest of Technical Papers 54, S1 (2023): 588–90. http://dx.doi.org/10.1002/sdtp.16361.

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VR content is a key link in building a VR ecosystem, but the extreme lack of high-quality content has become the core shortcoming restricting the development of the VR industry, so in the medium and long term, the VR industry will shift from hardware technology upgrades to high-quality content-oriented, and is expected to usher in a new round of growth driven by business model innovation and content explosion. With 3D reconstruction, users can experience virtual scenes visually and audibly. The development of 3D reconstruction technology will bring great changes to existing players, and also g

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Deng, Yujuan. "Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation." Advances in Mathematical Physics 2021 (November 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/7480422.

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This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial in

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Zhou, Ding, Guohua Wei, and Xiaojun Yuan. "Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques." Applied Sciences 14, no. 24 (2024): 11717. https://doi.org/10.3390/app142411717.

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This paper proposes a method for 3D reconstruction from Freehand Design Sketching (FDS) in architecture and industrial design. The implementation begins by extracting features from the FDS using the self-supervised learning model DINO, followed by the continuous Signed Distance Function (SDF) regression as an implicit representation through a Multi-Layer Perceptron network. Taking eyeglass frames as an example, the 2D contour and freehand sketch optimize the alignment by their geometrical similarity while exploiting symmetry to improve reconstruction accuracy. Experiments demonstrate that this

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Zhao, Feiyu. "A Novel Computational Paradigm for Reconstructing Solid CAD Features from a Segmented Manifold Triangular Mesh." Applied Sciences 14, no. 14 (2024): 6183. http://dx.doi.org/10.3390/app14146183.

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We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. We categorize four prevalent features, namely extrusion, rotation, sweep, and loft, as generalized swept bodies driven by cross-sectional sketches and feature paths, providing a unified mathematical representation for various feature types. The numerical optimiz

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Shi, Dingnan. "The Review of Research on 3D Model Reconstruction Based on Point Cloud Data." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 370–77. http://dx.doi.org/10.62051/v6yk2g06.

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Point cloud-based 3D modeling is a cutting-edge technology that leverages point cloud data from sensors like lidar and cameras to recreate detailed 3D models of objects and environments. This paper serves to outline the fundamental route, key principles, and prevalent methods within this field, with a specific emphasis on the preprocessing registration techniques for point cloud data and the advancements in 3D model reconstruction technologies.Furthermore, the article will delve into foundational 3D reconstruction methodologies, categorizing them into optimization-driven approaches and interpo

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Du, Xiaofu, Qiuming Zhu, Guoru Ding, et al. "UAV-Assisted Three-Dimensional Spectrum Mapping Driven by Spectrum Data and Channel Model." Symmetry 13, no. 12 (2021): 2308. http://dx.doi.org/10.3390/sym13122308.

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As the number of civil aerial vehicles increase explosively, spectrum scarcity and security become an increasingly challenge in both the airspace and terrestrial space. To address this difficulty, this paper presents an unmanned aerial vehicle-assisted (UAV-assisted) spectrum mapping system and a spectrum data reconstruction algorithm driven by spectrum data and channel model are proposed. The reconstruction algorithm, which includes a model-driven spectrum data inference method and a spectrum data completion method with uniformity decision mechanism, can reconstruct limited and incomplete spe

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Ren, Yong, Mingquan Zhou, Pengbo Zhou, et al. "Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image." Electronics 13, no. 21 (2024): 4264. http://dx.doi.org/10.3390/electronics13214264.

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Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing w

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Mitić, Predrag, Vladimir Kočović, Milan Mišić, et al. "A Novel Approach in 3D Model Reconstruction from Engineering Drawings Based on Symmetric Adjacency Matrices Using DXF Files and Genetic Algorithm." Symmetry 17, no. 5 (2025): 771. https://doi.org/10.3390/sym17050771.

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The application of CAD/CAM technologies in modern production has revolutionized manufacturing processes, leading to significant improvements in precision, efficiency, and flexibility. These technologies enable the design and manufacturing of complex geometries with high accuracy, reducing errors and material waste. CAD/CAM integration streamlines workflows, enhances productivity, and facilitates rapid prototyping, accelerating the time-to-market for new products. Additionally, it supports customization and scalability in production, allowing for cost-effective small-batch and large-scale manuf

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Tripodi, S., L. Duan, F. Trastour, V. Poujad, L. Laurore, and Y. Tarabalka. "AUTOMATED CHAIN FOR LARGE-SCALE 3D RECONSTRUCTION OF URBAN SCENES FROM SATELLITE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 243–50. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-243-2019.

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<p><strong>Abstract.</strong> Automatic city modeling from satellite imagery is a popular yet challenging topic in remote sensing, driven by numerous applications such as telecommunications, defence and urban mamagement. In this paper, we present an automated chain for large-scale 3D reconstruction of urban scenes with a Level of Detail 1 from satellite images. The proposed framework relies on two key ingredient. First, from a stereo pair of images, we estimate a digital terrain model and a digital height model, by using a novel set of feature descriptors based on multiscale

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Sadeghi, F., H. Arefi, A. Fallah, and M. Hahn. "3D BUILDING FAÇADE RECONSTRUCTION USING HANDHELD LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 625–30. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-625-2015.

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3D The three dimensional building modelling has been an interesting topic of research for decades and it seems that photogrammetry methods provide the only economic means to acquire truly 3D city data. According to the enormous developments of 3D building reconstruction with several applications such as navigation system, location based services and urban planning, the need to consider the semantic features (such as windows and doors) becomes more essential than ever, and therefore, a 3D model of buildings as block is not any more sufficient. To reconstruct the façade elements completely, we e

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Mahphood, A., and H. Arefi. "A DATA DRIVEN METHOD FOR FLAT ROOF BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 167–72. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-167-2017.

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3D building modeling is one of the most important applications in photogrammetry and remote sensing. Airborne LiDAR (Light Detection And Ranging) is one of the primary information sources for building modeling. In this paper, a new data-driven method is proposed for 3D building modeling of flat roofs. First, roof segmentation is implemented using region growing method. The distance between roof points and the height difference of the points are utilized in this step. Next, the building edge points are detected using a new method that employs grid data, and then roof lines are regularized using

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Ilarionov, Raycho, та Krasimir Krastev. "А System for Input of 3D Objects into Computing Environment". Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (8 серпня 2015): 17. http://dx.doi.org/10.17770/etr2013vol2.849.

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This paper proposes an approach for design and implementation of automated 3D scanner used for input of mechanical 3D objects into computing environment. The presented model of 3D scanner is based on kinematic diagram of positioning system with 5 degree of freedom – 3 linear and 2 rotational, each driven by servo motors. For distance measuring is used laser scanning head with rotational triangulation. The paper describes also algorithms for functional control of the scanning process, obtaining of point cloud, object reconstruction and export to standard CAD format. Keywords

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Zavar, H., H. Arefi, S. Malihi, and M. Maboudi. "TOPOLOGY-AWARE 3D MODELLING OF INDOOR SPACES FROM POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 267–74. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-267-2021.

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Abstract. In this paper we introduce a topology-aware data-driven approach for 3D reconstruction of indoor spaces, which is an active research topic with several practical applications. After separating floor and ceiling, segmentation is followed by computing the α-shapes of the segment. The adjacency graph of all α-shapes is used to find the intersecting planes. By employing a B-rep approach, an initial 3D model is computed. Afterwards, adjacency graph of the intersected planes which constitute the initial model is analyzed in order to refine the 3D model. This leads to a water-tight and topo

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Orthuber, E., and J. Avbelj. "3D BUILDING RECONSTRUCTION FROM LIDAR POINT CLOUDS BY ADAPTIVE DUAL CONTOURING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 157–64. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-157-2015.

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This paper presents a novel workflow for data-driven building reconstruction from Light Detection and Ranging (LiDAR) point clouds. The method comprises building extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment boundary creation, and a structural 3D building reconstruction approach using adaptive 2.5D Dual Contouring. First, a 2D-grid is overlain on the segmented point cloud. Second, in each grid cell 3D vertices of the building model are estimated from the corresponding LiDAR points. Then, the number of 3D vertices is reduced in a quad-tree colla

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Luo, Jianbin, Shaofei Jiang, Yamian Zeng, and Changqin Lai. "Three-Dimensional Reconstruction and Visualization of Underwater Bridge Piers Using Sonar Imaging." Sensors 24, no. 14 (2024): 4732. http://dx.doi.org/10.3390/s24144732.

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The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of brid

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Yang, Hao, An Qing You, Wen Wu Pan, and Hai Long Tang. "An Automatic Collision Avoidance System Based on LiDAR." Applied Mechanics and Materials 644-650 (September 2014): 952–56. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.952.

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For vehicle-borne LiDAR, a mathematical model is built for the computation and reconstruction of laser point cloud with the scanning data, GPS data and IMU data. 3D point cloud of the road and the scenery on the both sides of the road is obtained. Then according to the trajectory of the vehicle, 3D roaming for the scenery on the both sides of the road is realized using OpenGL 3D engine technology. This technology provides a probably feasible way for anti-collision of vehicles and aircrafts when driven at night, in the heavy fog or flying between the mountains.

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Tran, Ha, and Kourosh Khoshelham. "Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo." Remote Sensing 12, no. 5 (2020): 838. http://dx.doi.org/10.3390/rs12050838.

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Automated reconstruction of Building Information Models (BIMs) from point clouds has been an intensive and challenging research topic for decades. Traditionally, 3D models of indoor environments are reconstructed purely by data-driven methods, which are susceptible to erroneous and incomplete data. Procedural-based methods such as the shape grammar are more robust to uncertainty and incompleteness of the data as they exploit the regularity and repetition of structural elements and architectural design principles in the reconstruction. Nevertheless, these methods are often limited to simple arc

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Rojgar Qarani Ismael and Haval Sadeq. "LoD2 Building Reconstruction from Stereo Satellite Imagery using Deep Learning and Model-Driven Approach." Zanco Journal of Pure and Applied Sciences 37, no. 2 (2025): 103–18. https://doi.org/10.21271/zjpas.37.2.10.

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This study presents a Level of Detail 2 building reconstruction approach for open and occluded areas from stereo-satellite imagery. The approach combines deep learning techniques, and digital surface models with model-driven methodology. The best performance of deep learning algorithms (U-Net, FCN, and Mask R-CNN) for building boundary segmentation was selected and then integrated with model-driven technique for the purpose of accurate geometric building fitting employing digital surface model (DSM) generated by semi global matching. The Reconstructed model was refined by utilizing OpenStreetM

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Razali, Ahmad Firdaus, Kyna Lani Edward, Mohd Farid Mohd Ariff, Nurul Shahirah Jasni, Norhadija Darwin, and Zulkepli Majid. "Innovative Forensic Features and Blood Spatter 3D Reconstruction from Geospatial-driven Datasets." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23930–37. https://doi.org/10.48084/etasr.10327.

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Crime scene documentation serves as the basis for reconstructing and interpreting evidence to provide forensic evaluation of crime events. The reliance on 2D photography for forensic features does not employ extensive documentation, as it limits the analytical properties of the captured data, such as 3D data fusion accurate scaling and innovative augmentation. This paper presents an innovative method to reconstruct forensic features and blood spatter using geospatial-driven datasets from multiple sources. The procedure involves a 3D reconstruction of a crime scene via geospatial techniques, na

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Mahmoud, Mostafa, Wu Chen, Yang Yang, Tianxia Liu, and Yaxin Li. "Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 479–86. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-479-2024.

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Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete

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Mwangangi, K. K., P. O. Mc’Okeyo, S. J. Oude Elberink, and F. Nex. "EXPLORING THE POTENTIALS OF UAV PHOTOGRAMMETRIC POINT CLOUDS IN FAÇADE DETECTION AND 3D RECONSTRUCTION OF BUILDINGS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 433–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-433-2022.

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Abstract. The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstructi

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Zhang, Songchun, and Chunhui Zhao. "Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10112–20. https://doi.org/10.1609/aaai.v39i10.33097.

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Inferring 3D structures from sparse, unposed observations is challenging due to its unconstrained nature. Recent methods propose to predict implicit representations directly from unposed inputs in a data-driven manner, achieving promising results. However, these methods do not utilize geometric priors and cannot hallucinate the appearance of unseen regions, thus making it challenging to reconstruct fine geometric and textural details. To tackle this challenge, our key idea is to reformulate this ill-posed problem as conditional novel view synthesis, aiming to generate complete observations fro

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Qiao, Yiya, Xiaohuan Xi, Sheng Nie, Pu Wang, Hao Guo, and Cheng Wang. "Power Pylon Reconstruction from Airborne LiDAR Data Based on Component Segmentation and Model Matching." Remote Sensing 14, no. 19 (2022): 4905. http://dx.doi.org/10.3390/rs14194905.

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In recent years, with the rapid growth of State Grid digitization, it has become necessary to perform three-dimensional (3D) reconstruction of power elements with high efficiency and precision to achieve full coverage when simulating important transmission lines. Limited by the performance of acquisition equipment and the environment, the actual scanned point cloud usually has problems such as noise interference and data loss, presenting a great challenge for 3D reconstruction. This study proposes a model-driven 3D reconstruction method based on Airborne LiDAR point cloud data. Firstly, power

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Kada, M. "3D RECONSTRUCTION OF SIMPLE BUILDINGS FROM POINT CLOUDS USING NEURAL NETWORKS WITH CONTINUOUS CONVOLUTIONS (CONVPOINT)." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W4-2022 (October 14, 2022): 61–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w4-2022-61-2022.

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Abstract. The automatic reconstruction of 3D building models from airborne laser scanning point clouds or aerial imagery data in a model-driven fashion most often consists of a recognition of standardized building primitives with typically rectangular footprints and parameterized roof shapes based on a pre-defined collection, and a parameter estimation so that the selected primitives best fit the input data. For more complex buildings that consist of multiple parts, several such primitives need to be combined. This paper focuses on the reconstruction of such simple buildings, and explores the

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Wang, Cheng-Wei, and Chao-Chung Peng. "3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor." Sensors 21, no. 8 (2021): 2587. http://dx.doi.org/10.3390/s21082587.

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Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an eff

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Asdar, Sarah, Daniele Ciani, and Bruno Buongiorno Nardelli. "3D reconstruction of horizontal and vertical quasi-geostrophic currents in the North Atlantic Ocean." Earth System Science Data 16, no. 2 (2024): 1029–46. http://dx.doi.org/10.5194/essd-16-1029-2024.

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Abstract. In this paper we introduce a new high-resolution (1/10°) data-driven dataset of 3D ocean currents developed by the National Research Council of Italy in the framework of the European Space Agency World Ocean Circulation project: the WOC-NATL3D dataset. The product domain extends over a wide portion of the North Atlantic Ocean from the surface down to 1500 m depth, and the dataset covers the period between 2010 and 2019. To generate this product, a diabatic quasi-geostrophic diagnostic model is applied to data-driven 3D temperature and salinity fields obtained through a deep learning

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Fu, Chuanyu, Nan Huang, Zijie Huang, et al. "Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes." Remote Sensing 15, no. 9 (2023): 2474. http://dx.doi.org/10.3390/rs15092474.

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Multiview stereo (MVS) achieves efficient 3D reconstruction on Lambertian surfaces and strongly textured regions. However, the reconstruction of weakly textured regions, especially planar surfaces in weakly textured regions, still faces significant challenges due to the fuzzy matching problem of photometric consistency. In this paper, we propose a multiview stereo for recovering planar surfaces guided by confidence calculations, resulting in the construction of large-scale 3D models for high-resolution image scenes. Specifically, a confidence calculation method is proposed to express the relia

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Bonotto, Matteo, Daniele Evangelista, Marco Imperoli, and Alberto Pretto. "An Effective One-shot Body Part Multi-View Reconstruction Device with Self-calibration Capabilities." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W7-2024 (December 13, 2024): 33–39. https://doi.org/10.5194/isprs-archives-xlviii-2-w7-2024-33-2024.

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Abstract. This paper introduces a custom-built low-cost camera ring device designed for automatic cast synthesis, able to accurately and instantly scan body parts. The scanned mesh will be used as a backbone model for the cast design and 3D printing. The system is based on the multi-view active stereo principle and it is composed of a circular array of 16 synchronized cameras (Fig. 1) and 4 equally distributed IR pseudo-random laser pattern projectors. We employ a custom multi-view stereo reconstruction pipeline based on (Schönberger et al., 2016), which guarantees optimal results without the

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Yang, Xueyuan, Chao Yao, and Xiaojuan Ban. "Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10225–33. http://dx.doi.org/10.1609/aaai.v38i9.28888.

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Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely on sparse IMU data is inherently fraught with ambiguity, a consequence of numerous identical IMU readings corresponding to different poses. In this paper, we explore the spatial importance of sparse sensors, supervised by text that describes specific actions. Specifically, uncertainty is introduced

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Yuan, Zhenlong, Jiakai Cao, Zhaoxin Li, Hao Jiang, and Zhaoqi Wang. "SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 6871–80. http://dx.doi.org/10.1609/aaai.v38i7.28512.

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In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes and further leverage these constraints for pixelwise patch deformation on both matching cost and propagation. Concurrently, we propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths, significantly improving the completeness of r

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Özdemir, E., and F. Remondino. "CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 103–10. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-103-2019.

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<p><strong>Abstract.</strong> Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification

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Camacho, P. H. T., V. M. R. Santiago, and C. J. S. Sarmiento. "SEMI-AUTOMATIC GENERATION OF AN LOD1 AND LOD2 3D CITY MODEL OF TANAUAN CITY, BATANGAS USING OPENSTREETMAP AND TAAL OPEN LIDAR DATA IN QGIS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W6-2021 (November 18, 2021): 77–84. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w6-2021-77-2021.

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Abstract. 3D city models have found purpose beyond simple visualization of space by serving as building blocks of digital twins and smart cities. These are useful to urban areas in the Philippines through diversified applications: urban planning, disaster mitigation, environmental monitoring, and policy making. This study explored the use of free and open-source software to generate an LOD1 and LOD2 3D city model of Tanauan City, Batangas using building footprints from OpenStreetMap and elevation models from Taal Open LiDAR data. The proposed approach consists of GIS-based methods including da

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Lekadir, Karim, Matthias Lange, Veronika A. Zimmer, Corné Hoogendoorn, and Alejandro F. Frangi. "Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model." Medical Image Analysis 27 (January 2016): 105–16. http://dx.doi.org/10.1016/j.media.2015.03.006.

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Kullmann, Timo, Patrick Hübner, Tristan Wirth, Arjan Kuijper, and Dorota Iwaszczuk. "Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W8-2024 (December 14, 2024): 251–58. https://doi.org/10.5194/isprs-archives-xlviii-2-w8-2024-251-2024.

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Abstract. Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera’s pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have cho

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Yasin, Hashim, and Björn Krüger. "An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks." Sensors 21, no. 7 (2021): 2415. http://dx.doi.org/10.3390/s21072415.

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We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of join

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Kumar, Haribalan, Dragoş M. Vasilescu, Youbing Yin, Eric A. Hoffman, Merryn H. Tawhai, and Ching-Long Lin. "Multiscale imaging and registration-driven model for pulmonary acinar mechanics in the mouse." Journal of Applied Physiology 114, no. 8 (2013): 971–78. http://dx.doi.org/10.1152/japplphysiol.01136.2012.

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A registration-based multiscale method to obtain a deforming geometric model of mouse acinus is presented. An intact mouse lung was fixed by means of vascular perfusion at a hydrostatic inflation pressure of 20 cmH2O. Microcomputed tomography (μCT) scans were obtained at multiple resolutions. Substructural morphometric analysis of a complete acinus was performed by computing a surface-to-volume (S/V) ratio directly from the 3D reconstruction of the acinar geometry. A geometric similarity is observed to exist in the acinus where S/V is approximately preserved anywhere in the model. Using multis

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Xiao, Wang, Yifan Chen, Huisheng Zhang, and Denghai Shen. "Remaining Useful Life Prediction Method for High Temperature Blades of Gas Turbines Based on 3D Reconstruction and Machine Learning Techniques." Applied Sciences 13, no. 19 (2023): 11079. http://dx.doi.org/10.3390/app131911079.

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Turbine blades are crucial components exposed to harsh conditions, such as high temperatures, high pressures, and high rotational speeds. It is of great significance to accurately predict the life of blades for reducing maintenance cost and improving the reliability of gas turbine systems. A rapid and accurate blade life assessment method holds significant importance in the maintenance plan of gas turbine engines. In this paper, a novel on-line remaining useful life (RUL) prediction method for high-temperature blades is proposed based on 3D reconstruction technology and data-driven surrogate m

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He, Y., C. Zhang, and C. S. Fraser. "An energy minimization approach to automated extraction of regular building footprints from airborne LiDAR data." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (August 7, 2014): 65–72. http://dx.doi.org/10.5194/isprsannals-ii-3-65-2014.

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This paper presents an automated approach to the extraction of building footprints from airborne LiDAR data based on energy minimization. Automated 3D building reconstruction in complex urban scenes has been a long-standing challenge in photogrammetry and computer vision. Building footprints constitute a fundamental component of a 3D building model and they are useful for a variety of applications. Airborne LiDAR provides large-scale elevation representation of urban scene and as such is an important data source for object reconstruction in spatial information systems. However, LiDAR points on

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Wu, Xianyu, Penghao Li, Xin Zhang, Jiangtao Chen, and Feng Huang. "Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning." Sensors 23, no. 10 (2023): 4592. http://dx.doi.org/10.3390/s23104592.

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Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target’s surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mi

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Gong, Lanxin, Changhong Peng, and Qingyu Huang. "Deterministic Data Assimilation in Thermal-Hydraulic Analysis: Application to Natural Circulation Loops." Journal of Nuclear Engineering 6, no. 3 (2025): 23. https://doi.org/10.3390/jne6030023.

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Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two

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Ishii, Shin, Sehyung Lee, Hidetoshi Urakubo, Hideaki Kume, and Haruo Kasai. "Generative and discriminative model-based approaches to microscopic image restoration and segmentation." Microscopy 69, no. 2 (2020): 79–91. http://dx.doi.org/10.1093/jmicro/dfaa007.

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Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the r

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Dippold, Elisabeth Johanna, and Fuan Tsai. "Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation." Sensors 24, no. 7 (2024): 2358. http://dx.doi.org/10.3390/s24072358.

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The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for redu

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Zhang, Longwen, Chuxiao Zeng, Qixuan Zhang, et al. "Video-Driven Neural Physically-Based Facial Asset for Production." ACM Transactions on Graphics 41, no. 6 (2022): 1–16. http://dx.doi.org/10.1145/3550454.3555445.

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Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multi

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