3d reconstruction deep learning review This review explores the development of these techniques, highlighting key strategies, challenges, and future Jun 15, 2019 · 3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. In recent years, 3D reconstruction of single image using deep learning technology has achieved remarkable results. Other algorithms, such as object detection, semantic Feb 21, 2025 · Deep learning techniques have made great strides in 3D reconstruction, converting standard RGB images into high-quality 3D models with minimal effort. In this paper, we survey this rich and growing area. Traditional methods to Jan 22, 2024 · 3D reconstruction method, the 3D reconstruction method based on deep learning has more flexible input and output, and higher efficiency. com Mar 6, 2023 · We first describe various representations for 3D shapes in the deep learning context. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. This paper reviews deep learning-based methods in 3D reconstruction from single or multiple images. We reviewed the single image 3D reconstruction method based on deep learning more comprehensively, including the challenges faced by the method, reconstruction algorithms for different 3D representa- May 18, 2022 · In this work, we provide a state-of-the-art survey of deep learning-based single- and multi-view 3D object reconstruction methods. See full list on link. Sep 7, 2020 · The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. MVSNet [26] is the representative deep learning method for 3D reconstruction. In contrast, the focus of this paper is on the reviews of the applications of different deep-learning algorithms and architectures in reconstruction of single image or multiple images based on deep learning. Jan 1, 2023 · As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. With the development of deep learning and GPU List of projects for 3d reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done Sep 1, 2021 · The study [9] gives an extensive review of deep learning in medical image reconstruction but the paper focuses more on the mathematical models of several deep learning algorithms in medical image reconstruction. The processing of 3D data employs a wide range of strategies to deal with unique problems. Sep 3, 2020 · The reconstruction of 3D object from a single image is an important task in the field of computer vision. Infinitely many different 3D objects can be projected onto the same 2D plane, which makes the reconstruction May 30, 2022 · KEY WORDS: Deep Learning, Machine Learning, 3D City Models, 3D Reconstruction, LoD. This paper presents a comprehensive review, covering recent deep learning methods for multi-view stereo. Then we review the development of 3D mesh reconstruction methods from voxels, point clouds, single images, and multi-view images. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital 3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. edu. Recently, deep learning has been increasingly used to solve several 3D vision problems due to the predominating performance, including the multi-view stereo problem. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. Jan 4, 2025 · This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. Traditional methods to Aug 23, 2022 · Performing 3D reconstruction from a single 2D input is a challenging problem that is trending in literature. In this paper, the methods are grouped based on their shape representations Mar 7, 2024 · The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. It is believed that in the future, 3D reconstruction technology Dec 5, 2023 · Computed tomography (CT) is used in a wide range of medical imaging diagnoses. Jan 1, 2024 · This has sparked scholarly interest in exploring more collaborative efforts in this field. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Moreover, combining 3D reconstruction with deep learning algorithms has introduced new technologies for civil engineering. This review is different from the review by Ham et al. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images Feb 1, 2021 · This survey paper focuses on deep learning advances in 3D shape reconstruction and generation, specifically single-view reconstruction of 3D objects (scene reconstruction and organic shapes human faces and bodies reconstruction are beyond the scope of this paper), a topic that belongs to the 3D synthesis category and has attracted a lot of scientific attention in recent years. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving Dec 1, 2021 · It is one of the most important components of 3D reconstruction. 4)We provide a comparative summary of the prop-erties and performance of the reviewed methods Sep 7, 2024 · Reconstructing the three-dimensional structure of a scene is a classic and fundamental problem in computer vision, but it has been revolutionized by recent advancements in deep machine learning. The research scope includes single or multiple image sources but excludes RGB-D type input. Given this new era of rapid evolution, this article provides a May 14, 2024 · As expounded upon by Fu et al. . In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. Until recently, it was an ill-posed optimization problem, but with the advent of learning-based methods, the performance of 3D reconstruction has also significantly improved. springer. or Yuniaart et al. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Jul 11, 2024 · This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes, highlighting Neural Radiance Fields (NeRFs), Latent Diffusion Models (LDM), and 3D Gaussian Splatting. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in Sep 15, 2022 · For each 3D design, build a 30 × 30 × 30 3D grid, delineating each 3D grid as a binarized vector: meaning the voxel is inside the mesh, 0 means the voxel is not inside the mesh. [6], the realm of single-image 3D reconstruction, guided by depth learning techniques, grapples with six primary predicaments: (1) reconstruction of complicated shape, (2) uncertainties in object reconstruction, (3) intricate object details reconstruction, (4) computation time and memory constraints, (5 Apr 5, 2024 · With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. Several methods and their significance are discussed, also some challenges and research opportunities are proposed for further research directions. Traditional methods to reconstruct 3D object from a single image require prior knowledge and assumptions, and the reconstruction object is limited to a certain category or it Jan 28, 2023 · The reconstruction of 3D object from a single image is an important task in the field of computer vision. In a broad sense, 3D reconstruction methods take single or multiple 2D images to model shapes with different representations such as: voxels, meshes, point clouds and implicit functions. Feb 1, 2021 · However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. Given this new era of rapid evolution, this article provides a Jan 1, 2021 · AbstractThe reconstruction of 3D object from a single image is an important task in the field of computer vision. May 14, 2024 · Advancements in deep learning have revolutionized multi-view 3D reconstruction by enabling end-to-end 3D shape inferencing without the need for sequential feature matching typically found in conventional algorithms. 3D ShapeNets proposes a deep convolutional network for teaching voxelized shapes of 3D stencils in 3D meshes. tr Jan 1, 2023 · As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. Mar 7, 2024 · In this review paper, we concentrate on deep learning methods for reconstruction, augmentation, and registration in three dimensions. Contribute to natowi/3D-Reconstruction-with-Deep-Learning-Methods development by creating an account on GitHub. sightful analysis on all aspects of 3D reconstruction using deep learning, including the training data, the choice of network architectures and their effect on the 3D reconstruction results, the training strategies, and the application scenarios. ABSTRACT: 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW Mehmet Buyukdemircioglu 1, 2, 4, Sultan Kocaman 2, 3 *, Martin Kada 4 1 Hacettepe University, Graduate School of Science and Engineering, Ankara, Turkey - mbuyukdemircioglu@hacettepe. hbotpy shoyfg aqba yzhqc csjkm zizjuam xijs daun leqkx nlw emx ybbm vcnc lhkia cpe