Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"3D deep learning"”.
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Process Digitalization: 3D Deep Learning in Manufacturing Applications
… company. As well, given the importance of 3D data in the industry, the research also conducts a deep dive on working with, analyzing, and integrating 3D data into an AI model using various techniques, from statistical analysis to 3D deep learning. Discussion on the different data …
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3D Deep Learning for Object-Centric Geometric Perception
… aims at extracting the geometric attributes of 3D objects. These attributes include shape, pose, and motion of the target objects, which enable fine-grained object-level understanding for various tasks in graphics, computer vision, and robotics. With the growth of 3D geometry data and 3D deep …
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A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans
This paper introduces 3D-DDnet, a cutting-edge 3D deep learning (DL) framework designed to improve the image quality of low-dose computed tomography (LDCT) scans. Although LDCT scans are advantageous for reducing radiation exposure, they inherently suffer from reduced image quality. Our novel 3D DL …
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BRIDGING INTERPRETABILITY AND PERFORMANCE IN 3D DEEP LEARNING THROUGH GEOMETRIC INDUCTIVE BIASES
… possano risolvere limitazioni fondamentali nel deep learning 3D, in particolare per applicazioni critiche di sicurezza come l’ispezione delle reti elettriche. Nonostante i progressi significativi nel campo del 3D scene understanding, la maggior parte dei modelli allo stato dell’arte funziona …
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3D Deep Learning Segmentation for Fiber Break Analysis of Carbon Fiber Reinforced Polymer Tomograms
… of CFRPs under stress, enabling real-time 3D observation of the material’s failure. Due to the data-rich nature of the 3D scans at each time step, such experiments can result in thousands of 2D images per scan and multiple scans at different time- or loading-steps per test. This accumulates …
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H3DNET: A Deep Learning Framework for Hierarchical 3D Object Classification
Deep learning has received a lot of attention in the fields such as speech recognition and image classification because of the ability to learn multiple levels of features from raw data. However, 3D deep learning is relatively new but in high demand with their great research values. Current …
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Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
… and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many …
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3D diffusion based generation model for point cloud annotation and generation
… with the rapid advancement of applications and 3D scanning sensors, the demand for 3D deep learning based technology and data has increased dramatically. Especially 3D shape with semantic labels plays a significant role in 3D vision problems, such as auto-driven, 3D object detection and 3D scene …
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Learning 3D Representations from Data
Deep learning has achieved tremendous progress and success in processing images and natural languages. Deep models enable human-level perception, photorealistic image generation, and conversational language understanding. Despite significant progress, existing deep models still fail to meet the …
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Advancing 3D Segmentation: Deep Learning Techniques for Video and Medical Imaging
… advancements, extending these successes to 3D segmentation presents a unique set of challenges. The complexity inherent in 3D data, whether it be through temporal sequences in video analysis or another spatial dimension in medical imaging, demands innovative approaches that can accurately …
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Trimačio objekto erdvinio modelio sukūrimas iš dvimačių vaizdų naudojant mašininį mokymąsi /
… well-established methods to reconstruct 3D objects from various selected yet limited 2D image sets. We utilize synthetically generated views of textured meshes rendered with PyTorch3D [2] with variable image quality, as well as photographs of objects from the natural world. Reconstructing …
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Scalable and Generalizable Robot Learning: from Simulated to Real-World Applications
L'abstract è presente nell'allegato / the abstract is in the attachment
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Learning Clinical Body Composition Metrics from 2D and 3D Optical Imaging
… applications within computer graphics including 3D animation, virtual tailoring, ergonomic engineering, and virtual reality reconstruction. For clinical researchers working at the intersection of computer graphics, machine learning, and obesity-related epidemiology, computational modeling of …
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3D Visual Learning for Real-World Scenarios
L'abstract è presente nell'allegato / the abstract is in the attachment