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Auswahl der wissenschaftlichen Literatur zum Thema „3D model-Driven reconstruction“
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Zeitschriftenartikel zum Thema "3D model-Driven reconstruction"
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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 transformers, significantly raising the performance ceiling for HSI reconstruction. In this paper, we propose a novel joint SSM network named Sp3ctralMamba for HSI reconstruction. Sp3ctralMamba integrates frequency domain knowledge and physical priors to enhance reconstruction quality. Specifically, we first perform hierarchical decomposition of the 3D HSI embedding to mitigate the negative impact of distant bands on reconstruction. Next, we design a joint SSM block S3Mamba (S3MAB) to perform parallel scans of the embeddings from different bands. In addition to the conventional vanilla scan, S3MAB introduces a local scanning scheme to address the reconstruction challenges posed by the spatial sparsity of spectral information. Furthermore, a spiral scanning scheme in the frequency domain is incorporated to enhance the order correlation between different frequency signals. Finally, we introduce energy priors and structural priors to constrain the generation of spectral and spatial representations during the training process. Extensive experiments on both simulated and real datasets demonstrate that Sp3ctralMamba significantly elevates HSI reconstruction performance to a new level, surpassing SOTA methods in both quantitative and qualitative metrics.
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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 are obtained by clustering the line segment intersection points. Second, the pylon model library is designed to assist in pylon reconstruction. In the predefined pylon model library, a pylon is divided into two parts: pylon body and pylon head. Before pylon reconstruction, the pylon type is identified by the inner distance shape context (IDSC) algorithm, which matches the shape contours of pylon extracted from UAV images and the projected pylon model. With the a priori shape and coplanar constraint, the line segments on pylon body are matched and the pylon body is modeled by fitting four principle legs and four side planes. Then a Markov Chain Monte Carlo (MCMC) sampler is used to estimate the parameters of the pylon head by computing the maximum probability between the projected model and the extracted line segments in images. Experimental results on several UAV image datasets show that the proposed method is a feasible way of automatically reconstructing the pylon.
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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 results illustrate the issues of Perception (models allow accurate reconstruction of 3D objects from a limited set of X-ray projections), Decision (models allow to take into account elastic and dynamic characteristics of muscles), and Action (models allow to design innovative navigational and robotics aids to the realization of complex interventions). Likewise, models play a major role in the process of surgeon’s education, which leads to the concept of Virtual Orthopedic University. Conclusions: Model Driven Therapy emerges as the way to perform optimal medical and surgical interventions, providing physicians and surgeons with the possibility to augment their capacities of sensing multi-modal information, of combining them to define optimal strategies, and of performing accurate and safe actions.
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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 models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5 ± 1.1 mm. The best error range is 1.9 ± 1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.
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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 iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC.
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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 reconstructability as a function of the proxy geometry , a set of viewpoints, and optionally a series of scene images acquired in flight. To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning. We demonstrate that our data-driven reconstructability predictions are more closely correlated to the true reconstruction quality than prior heuristic measures. Further, our learned predictor can be easily integrated into existing path planners to yield improvements. Finally, we devise a new iterative view planning framework, based on the learned reconstructability, and show superior performance of the new planner when reconstructing both synthetic and real scenes.
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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 reconstruction quality. This paper introduces a novel quality-driven planning method for satellite stereo imaging, aiming at optimizing the search of stereo pairs to achieve high-quality 3D reconstruction. Moreover, a regression model is customized and incorporated to estimate the reconstructed point cloud geopositioning quality, based on the enhanced features of possible Earth-imaging opportunities. Experiments conducted on both real satellite images and simulated constellation data demonstrate the efficacy of the proposed method in estimating reconstruction quality beforehand and searching for optimal stereo pair combinations as the final satellite imaging schedule, which can improve the stereo quality significantly.
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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 greatly promote the rapid development of metaverse content Through continuous algorithm improvement, 3D reconstruction continues to be applied to all aspects of life.
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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 intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method.
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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 method can effectively reconstruct high-quality 3D models of eyeglass frames from 2D freehand sketches, outperforming existing deep learning-based 3D reconstruction methods. This research offers practical information for understanding 3D modeling methodology for FDS, triggering multiple modes of design creativity and efficient scheme adjustments in industrial or architectural conceptual design. In conclusion, this novel approach integrates self-supervised learning and geometric optimization to achieve unprecedented fidelity in 3D reconstruction from FDS, setting a new benchmark for AI-driven design processes in industrial and architectural applications.
Dissertationen zum Thema "3D model-Driven reconstruction"
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Jin, Nan. "ModSETS : a model-driven stereo eye tracking system : application in the medical field." Electronic Thesis or Diss., Aix-Marseille, 2020. http://www.theses.fr/2020AIXM0339.
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La plupart des systèmes de suivi de l’œil existants ne fournissent que des analyses précises et en temps réel des mouvements oculaires 2D (horizontaux et verticaux) dans des conditions de laboratoire. Ils sont en général insuffisants pour les applications du domaine médical, parce que leur robustesse est souvent mise à l'épreuve dans la pratique et la mesure du mouvement torsionnel de l’œil est ignorée la plupart du temps. Cela augmente la difficulté d'interprétation des données collectées et peut donc affecter la qualité du diagnostic médical. Un système de suivi de l’œil en stéréophotogrammétrie piloté par modèle (ModSETS) est proposé dans cette thèse de doctorat, pour offrir une analyse précise, robuste et en temps réel des mouvements oculaires 3D (horizontaux, verticaux et torsionnels) pour les applications médicales. La performance de ModSETS dans le suivi des mouvements oculaires 2D est démontrée par un ⟪gaze test⟫. Il a montré une bonne précision (i.e., d'environ 1 °) dans l'estimation du regard qui est conforme aux exigences de nombreuses applications médicales. La robustesse de ModSETS dans des conditions normales d’utilisation est également confirmée, traduit par un taux de réussite élevé dans la segmentation de la pupille (i.e., 91,4%). Certains résultats encourageants ont été obtenus dans la mesure des mouvements torsionnels de l’œil, même s'il est difficile d’effectuer une évaluation quantitative avec le matériel actuel. Le principe d’un tel système de suivi de l’œil en stéréophotogrammétrie piloté par modèle (ModSETS) est validé. Il montre un grand potentiel dans le suivi des mouvements oculaires 3D pour les applications du domaine médical<br>Most current eye tracking systems only provide accurate and real-time analysis of 2D (horizontal and vertical) eye movement in laboratory conditions. It is usually insufficient for medical applications, because their robustness is often challenged in practice and the measurement of eye torsion is almost ignored. This increases the difficulty of data interpretation and may thus affect the quality of medical diagnosis. A Model-driven Stereo Eye Tracking System (ModSETS) is proposed in this Ph.D. thesis, to provide accurate, robust, and real-time analysis of 3D (horizontal, vertical and torsional) eye movement for medical applications. The performance of ModSETS in 2D eye movement tracking is proved through a gaze test. It showed a good accuracy (i.e., of about 1°) in gaze estimation that is compliant with the requirements of many medical applications. The robustness of ModSETS in practical conditions is also confirmed, which is reflected by a high success rate in pupil segmentation (i.e., 91.4%). Some encouraging results of eye torsion measurement were obtained, even though it is difficult to make a quantitative assessment with current hardware. Therefore, the principle of ModSETS (Model-driven Stereo Eye Tracking System) is validated and shows great potential in 3D eye movement tracking for medical applications
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Partovi, Tahmineh. "3D Building Model Reconstruction from Very High Resolution Satellite Stereo Imagery." Doctoral thesis, 2019. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201910022067.
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Automatic three-dimensional (3D) building model reconstruction using remote sensing data is crucial in applications which require large-scale and frequent building model updates, such as disaster monitoring and urban management, to avoid huge manual efforts and costs. Recent advances in the availability of very high-resolution satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for 3D building model reconstructions. In this dissertation, a novel multistage hybrid automatic 3D building model reconstruction approach is proposed which reconstructs building models in level of details 2 (LOD2) based on digital surface model (DSM) data generated from the very high-resolution stereo imagery of the WorldView-2 satellite. This approach uses DSM data in combination with orthorectified panchromatic (PAN) and pan-sharpened data of multispectral satellite imagery to overcome the drawbacks of DSM data, such as blurred building boundaries, rough building shapes unwanted failures in the roof geometries. In the first stage, the rough building boundaries in the DSM-based building masks are refined by classifying the geometrical features of the corresponding PAN images. The refined boundaries are then simplified in the second stage through a parameterization procedure which represents the boundaries by a set of line segments. The main orientations of buildings are then determined, and the line segments are regularized accordingly. The regularized line segments are then connected to each other based on a rule-based method to form polygonal building boundaries. In the third stage, a novel technique is proposed to decompose the building polygons into a number of rectangles under the assumption that buildings are usually composed of rectangular structures. In the fourth stage, a roof model library is defined, which includes flat, gable, half-hip, hip, pyramid and mansard roofs. These primitive roof types are then assigned to the rectangles based on a deep learning-based classification method. In the fifth stage, a novel approach is developed to reconstruct watertight parameterized 3D building models based on the results of the previous stages and normalized DSM (nDSM) of satellite imagery. In the final stage, a novel approach is proposed to optimize building parameters based on an exhaustive search, so that the two-dimensional (2D) distance between the 3D building models and the building boundaries (obtained from building masks and PAN image) as well as the 3D normal distance between the 3D building models and the 3D point clouds (obtained from nDSM) are minimized. Different parts of the building blocks are then merged through a newly proposed intersection and merging process. All corresponding experiments were conducted on four areas of the city of Munich including 208 buildings and the results were evaluated qualitatively and quantitatively. According to the results, the proposed approach could accurately reconstruct 3D models of buildings, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provided a high level of automation by the limited number of primitive roof model types required and by performing automatic parameter initialization. In addition, the proposed boundary refinement method improved the DSM-based building masks specified by 8 % in area accuracy. Furthermore, the ridge line directions and roof types were detected accurately for most of the buildings. The combination of the first three stages improved the accuracy of the building boundaries by 70 % in comparison to using line segments extracted from building masks without refinement. Moreover, the proposed optimization approach could achieve in most cases the best combinations of 2D and 3D geometrical parameters of roof models. Finally, the intersection and merging process could successfully merge different parts of the complex building models.
Buchteile zum Thema "3D model-Driven reconstruction"
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Berretti, Stefano, Alberto Del Bimbo, and Pietro Pala. "3D Face Reconstruction from Two Orthogonal Images for Face Recognition Applications." In Multimedia Storage and Retrieval Innovations for Digital Library Systems. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0900-6.ch012.
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In this paper, an original hybrid 2D-3D face recognition approach is proposed using two orthogonal face images, frontal and side views of the face, to reconstruct the complete 3D geometry of the face. This is obtained using a model based solution, in which a 3D template face model is morphed according to the correspondence of a limited set of control points identified on the frontal and side images in addition to the model. Control points identification is driven by an Active Shape Model applied to the frontal image, whereas subsequent manual assistance is required for control points localization on the side view. The reconstructed 3D model is finally matched, using the iso-geodesic regions approach against a gallery of 3D face scans for the purpose of face recognition. Preliminary experimental results are provided on a small database showing the viability of the approach.
Konferenzberichte zum Thema "3D model-Driven reconstruction"
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Hanajik, Milan. "Efficient data-driven algorithm for the model-based 3D scene reconstruction from perspective images." In Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, edited by Heinrich Ebner, Christian Heipke, and Konrad Eder. SPIE, 1994. http://dx.doi.org/10.1117/12.182824.
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Partovi, Tahmineh, Thomas Kraus, Hossein Arefi, Mohammad Omidalizarandi, and Peter Reinartz. "Model-driven 3D building reconstruction based on integeration of DSM and spectral information of satellite images." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947150.
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Xu, Baixin, Jiarui Zhang, Kwan-Yee Lin, Chen Qian, and Ying He. "Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01643.
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Hu, Yazhe, and Tomonari Furukawa. "A Self-Supervised Learning Technique for Road Defects Detection Based on Monocular Three-Dimensional Reconstruction." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98135.
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Abstract This paper presents a self-supervised learning technique for road surface defects detection using a monocular camera. The uniqueness of the proposed technique relies on its self-supervised learning structure which is achieved by combining physics-driven three-dimensional (3D) reconstruction with data-driven Convolutional Neural Network (CNN). Only images from one camera are needed as the inputs to the model without human labeling. The 3D point cloud are reconstructed from input images based on a near-planar road 3D reconstruction process to self-supervise the learning process. During testing, the network receives images and predicts the images as defect or non-defect. A refined class prediction is produced by combining the 3D road surface data with the network output when the belief of original network prediction is not strong enough to conclude the classification. Experiments are conducted on real road surface images to find the optimal parameters for this model. The testing results demonstrate the robustness and effectiveness of the proposed self-supervised road surface defects detection technique.
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"ONTOLOGY-DRIVEN 3D RECONSTRUCTION OF ARCHITECTURAL OBJECTS." In 3D Model Aquisition and Representation. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002047300470054.
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Reck, Michaela, Marc Hilbert, René Hilhorst, and Thomas Indinger. "Comparison of Deep Learning Architectures for Dimensionality Reduction of 3D Flow Fields of a Racing Car." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0862.
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<div class="section abstract"><div class="htmlview paragraph">In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models. The resulting data from the 3D-DL study are compared to a traditional dimensionality reduction method, namely Proper Orthogonal Decomposition (POD). Flow field features are examined by using methods of local feature importance, aiming for awareness of predominant fluidic phenomena. We show that our data-driven models capture aerodynamically relevant zones around the racing car. 3D-DL architectures can represent complex nonlinear dependencies in the flow domain. The U-Net network demonstrates an <i>R</i><sup>2</sup> reconstruction accuracy of 99.94%, outperforming the results achieved from linear POD with an <i>R</i><sup>2</sup> of 99.57%. Efficiently handling numerous CFD simulations leads to improved post-processing and an accelerated investigation procedure for future aerodynamic development. Finally, the discovered findings provide further knowledge for the serial development to increase efficiency, thereby extending, e.g., the range of electric vehicles.</div></div>
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Omar, Saad, Diogo Salim, Mikhail Zaslavsky, and Lin Liang. "High-Resolution 3D Reservoir Mapping and Geosteering Using Voxel-Based Inversion Processing of UDAR Measurements." In 2024 SPWLA 65th Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2024. https://doi.org/10.30632/spwla-2024-0092.
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Ultradeep azimuthal resistivity (UDAR) measurements provide full three-dimensional (3D) volumetric sensitivity to the surrounding formation and are routinely used for strategic geosteering, reservoir navigation, reservoir characterization, and real-time drilling decision making based on the inverted resistivity profile. The initial layered-earth-model (1D) reservoir map can be further refined (on demand) with two-dimensional (2D) and 3D processing. Existing 3D processing, employing octree gridding for modeling and inversion, leads to extreme smoothing in the formation mapping with artifacts appearing away from the wellbore, thereby compromising the higher resolution and superior accuracy of 2D imaging inversions (either along the wellbore to image longitudinal discontinuities or at an arbitrary alignment angle with the wellbore to image lateral). High-resolution and accurate 3D reservoir mapping is thus critical for delivering precise drilling performance to increase asset recovery, improve wellbore quality, reduce overall well construction costs, and optimize production. In this paper, we present a new voxel-based processing method that provides sensitivity-driven, high-resolution 3D deep resistivity profiles and is, therefore, able to map arbitrary 3D reservoir heterogeneities. The minimally biased algorithm uses a non-uniform 3D voxel discretization of the imaging plane and the full 3D sensitivities of deep-directional resistivity measurements to map the 3D resistivity distribution. A full 3D electromagnetic simulator modeling arbitrary geometry and anisotropy (with exact gridding) is used in the inversion loop to accurately reconstruct the response, and preserving the resolution with an adaptive regularization enforces consistency and avoids data overfitting. In addition, a structure similarity regularization enhances the obtained anisotropy from the inversion process. The increased complexity of formation reconstruction from 1D to 3D requires not only sophistication in algorithmic development but also in tool measurements. The inversion has been validated on several 3D synthetic scenarios with various complexities, including avoiding laterally occurring shale bodies, imaging laterally displaced multifingered sand injectites, and laterally imaging residual oil bodies in shaly environments. The 3D processing preserves the resolution and interpretation consistency with 1D and 2D processing and successfully enhances the imaging of the approaching heterogeneities by quantifying their dimensions and orientation with respect to the wellbore. The workflow has also been applied to multiple deep-directional resistivity field data sets, demonstrating the ability to map arbitrary 3D heterogeneities with high resolution and precision.
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Wang, Zihan, Austin Bray, Kiarash Naghavi Khanghah, and Hongyi Xu. "A Generative Graph Neural Network-Based Framework for Designing Connectivity-Guaranteed Porous Metamaterial Units." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-143200.
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Abstract Porous metamaterial units filled with fluid have been used in engineering systems due to their ability to achieve desired properties (e.g., effective thermal conductvity). Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. In this study, we propose a generative graph neural network-based framework for designing the porous metamaterial units infilled with liquid. Firstly, we propose a graph-based metamaterial unit generation approach to generate porous metamaterial samples with complete connectivity in both solid and pore phases. Secondly, we establish and evaluate three distinct variational graph autoencoder (VGAE)-based generative models to assess their effectiveness in generating an accurate latent space representation of metamaterial structures. By choosing the model with the highest reconstruction accuracy, the property-driven design search is conducted to obtain novel metamaterial unit designs with the targeted properties. A case study on designing liquid-filled metamaterials for thermal conductivity properties is carried out. The effectiveness of the proposed graph neural network-based design approach is evaluated by comparing the performances of the obtained designs with those of existing designs in the training database. Merits and shortcomings of the proposed framework are also discussed.
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Akram Abdulrahman, Raghad, and Emad Hani Ismaeel. "4D Representation of the Built Heritage Post-Conflict: Interactive Modeling of the Qattanin Mosque in Old Mosul." In 5th International Conference on Architectural and Civil Engineering Sciences (CIC-ICACE'25). Cihan University-Erbil, 2025. https://doi.org/10.24086/icace2025/paper.1640.
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Abstract—The integration of technology in the domain of urban heritage management has witnessed a significant surge, driven by the multifaceted advantages it offers for the administration of conservation efforts. Among the most prominent technological advancements is the application of virtual reality (VR) systems, which facilitate digital preservation through the creation of detailed 3D models of historical edifices. These models can then be embedded within interactive environments, such as those provided by augmented reality (AR) systems. AR enables users to identify and explore historical buildings via tablets and mobile devices, enhancing accessibility and engagement. However, there is a notable dearth of research focused on the interactive reconstruction of historic monuments in the Mosul old city post-conflict. This study aims to address this gap by developing a methodological framework for constructing an interactive model of lost or damaged historical landmarks. The proposed approach involves linking these models to relevant historical texts and sources, allowing users to visualize a three-dimensional representation of the landmark on their mobile devices. This is achieved through the scanning of digital pages and the utilization of the Unite AR application, thereby fostering a more immersive and educational experience in the preservation and understanding of cultural heritage. Keywords: Augmented reality, built heritage, preservation, damaged building, post-conflict.
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Amerinatanzi, Amirhesam, Narges Shayesteh Moghaddam, Hamdy Ibrahim, and Mohammad Elahinia. "Evaluating a NiTi Implant Under Realistic Loads: A Simulation Study." In ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/smasis2016-9287.
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Additive manufacturing (i.e. 3D printing) has only recently be shown as a well-established technology to create complex shapes and porous structures from different biocompatible metal powder such as titanium, nitinol, and stainless steel alloys. This allows for manufacturing bone fixation hardware with patient-specific geometry and properties (e.g. density and mechanical properties) directly from CAD files. Superelastic NiTi is one of the most biocompatible alloys with high shock absorption and biomimetic hysteresis behavior. More importantly, NiTi has the lowest stiffness (36–68 GPa) among all biocompatible alloys [1]. The stiffness of NiTi can further be reduced, to the level of the cortical bone (10–31.2 GPa), by introducing engineered porosity using additive manufacturing [2–4]. The low level of fixation stiffness allows for bone to receive a stress profile close to that of healthy bone during the healing period. This enhances the bone remodeling process (Wolf’s Law) which primarily driven by the pattern of stress. Also, this match in the stiffness of bone and fixation mitigates the problem of stress shielding and detrimental stress concentrations. Stress shielding is a known problem for the currently in-use Ti-6Al-4V fixation hardware. The high stiffness of Ti-6Al-4V (112 GPa) compared to bone results in the absence of mechanical loading on the adjacent bone that causes loss of bone mass and density and subsequently bone/implant failure. We have proposed additively manufactured porous NiTi fixation hardware with a patient-specific stiffness to be used for the mandibular reconstructive surgery (MRS). In MRS, the use of metallic fixation hardware and double barrel fibula graft is the standard methodology to restore the mandible functionality and aesthetic. A validated finite element model was developed from a dried cadaveric mandible using CT scan data. The model simulated a patient’s mandible after mandibular reconstructive surgery to compare the performance of the conventional Ti-6Al-4V fixation hardware with the proposed one (porous superelastic NiTi fixation plates). An optimized level of porosity was determined to match the NiTi equivalent stiffness to that of a resected bone, then it was imposed to the simulated fixation plates. Moreover, the material property of superelastic NiTi was simulated by using a validated customized code. The code was calibrated by using DSC analysis and mechanical tests on several prepared bulk samples of Ni-rich NiTi. The model was run under common activities such as chewing by considering different levels of the applied fastening torques on screws. The results show a higher level of stress distribution on mandible cortical bone in the case of using NiTi fixation plates. Based on wolf’s law it can lead to a lower level of stress shielding on the grafted bone and over time bone can remodel itself. Moreover, the results suggest an optimum fastening torque for fastening the screws for the superelastic fixations causes more normal distribution of stress on the bone similar to that for the healthy mandible. Finally, we successfully fabricated the stiffness-matched porous NiTi fixation plates using selective laser melting technique, and they were mounted on the dried cadaveric mandible used to create the finite element model.