scholarly journals Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhou Lu
Keyword(s):  
3D Reconstruction ◽  
Construction Projects ◽  
3D Structure ◽  
3D Models ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Schedule Management ◽  
Reconstruction Methods ◽  
Low Efficiency ◽  
Construction Schedule

The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.

scholarly journals 3D RECONSTRUCTION FROM MULTI-VIEW MEDICAL X-RAY IMAGES – REVIEW AND EVALUATION OF EXISTING METHODS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 319-326 ◽  
Author(s):  
S. Hosseinian ◽  
H. Arefi
Keyword(s):  
3D Reconstruction ◽  
Imaging Techniques ◽  
Weight Bearing ◽  
Computer Assisted Surgery ◽  
3D Models ◽  
Alternative Methods ◽  
Computer Assisted ◽  
X Ray ◽  
Advantages And Disadvantages ◽  
Reconstruction Methods

The 3D concept is extremely important in clinical studies of human body. Accurate 3D models of bony structures are currently required in clinical routine for diagnosis, patient follow-up, surgical planning, computer assisted surgery and biomechanical applications. However, 3D conventional medical imaging techniques such as computed tomography (CT) scan and magnetic resonance imaging (MRI) have serious limitations such as using in non-weight-bearing positions, costs and high radiation dose(for CT). Therefore, 3D reconstruction methods from biplanar X-ray images have been taken into consideration as reliable alternative methods in order to achieve accurate 3D models with low dose radiation in weight-bearing positions. Different methods have been offered for 3D reconstruction from X-ray images using photogrammetry which should be assessed. In this paper, after demonstrating the principles of 3D reconstruction from X-ray images, different existing methods of 3D reconstruction of bony structures from radiographs are classified and evaluated with various metrics and their advantages and disadvantages are mentioned. Finally, a comparison has been done on the presented methods with respect to several metrics such as accuracy, reconstruction time and their applications. With regards to the research, each method has several advantages and disadvantages which should be considered for a specific application.

scholarly journals Enhancement of RGB-D Image Alignment Using Fiducial Markers

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1497 ◽  
Author(s):  
Tiago Madeira ◽  
Miguel Oliveira ◽  
Paulo Dias
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
3D Models ◽  
Difficult Problem ◽  
Image Alignment ◽  
Fiducial Markers ◽  
Final Model ◽  
Optimization Framework ◽  
Reconstruction Methods ◽  
Alignment Errors

Three-dimensional (3D) reconstruction methods generate a 3D textured model from the combination of data from several captures. As such, the geometrical transformations between these captures are required. The process of computing or refining these transformations is referred to as alignment. It is often a difficult problem to handle, in particular due to a lack of accuracy in the matching of features. We propose an optimization framework that takes advantage of fiducial markers placed in the scene. Since these markers are robustly detected, the problem of incorrect matching of features is overcome. The proposed procedure is capable of enhancing the 3D models created using consumer level RGB-D hand-held cameras, reducing visual artefacts caused by misalignments. One problem inherent to this solution is that the scene is polluted by the markers. Therefore, a tool was developed to allow their removal from the texture of the scene. Results show that our optimization framework is able to significantly reduce alignment errors between captures, which results in visually appealing reconstructions. Furthermore, the markers used to enhance the alignment are seamlessly removed from the final model texture.

scholarly journals Semi-supervised Three-dimensional Reconstruction Framework with GAN

2019 ◽  
Author(s):  
Chong Yu
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
3D Models ◽  
Generative Adversarial Network ◽  
Weak Supervision ◽  
Adversarial Network ◽  
Current State ◽  
Reconstruction Performance ◽  
Dimensional Reconstruction ◽  
Intrinsic Complexity

Because of the intrinsic complexity in computation, three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications. The existing methods for 3D reconstruction often produce holes, distortions and obscure parts in the reconstructed 3D models, or can only reconstruct voxelized 3D models for simple isolated objects. So they are not adequate for real usage. From 2014, the Generative Adversarial Network (GAN) is widely used in generating unreal dataset and semi-supervised learning. So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle. We propose a novel semi-supervised 3D reconstruction framework, namely SS-3D-GAN, which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge. This new model only takes real-time 2D observation images as the weak supervision, and doesn't rely on prior knowledge of shape models or any referenced observations. Finally, through the qualitative and quantitative experiments & analysis, this new method shows compelling advantages over the current state-of-the-art methods on Tanks & Temples reconstruction benchmark dataset.

scholarly journals Reviewing Construction Schedule Float Management

2017 ◽  
Vol 11 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Jyh-Bin Yang
Keyword(s):  
Construction Projects ◽  
Critical Path ◽  
Management Systems ◽  
Knowledge Level ◽  
Schedule Control ◽  
Incorrect Decision ◽  
Schedule Management ◽  
Professional Project ◽  
Construction Schedule

As the complexity in construction projects increased, simply managing the obtained float values and critical path(s) by the CPM method usually results in more difficult schedule control and, consequently, in incorrect decision making due to non-realistic float values. This study thoroughly reviewed various float types in the literature and professional project management systems, and discussed five managerial essentials and three proactive strategies on mitigating challenging float-related problems based on the perspective of managing schedules by controlling floats. With some comments and suggestions, the outcomes of this study not only improve the knowledge level on schedule management but also provide a better understanding of float management to improve the quality of schedule management.

IMAGE BASED 3D FACE RECONSTRUCTION: A SURVEY

2009 ◽  
Vol 09 (02) ◽  
pp. 217-250 ◽  
Author(s):  
GEORGIOS STYLIANOU ◽  
ANDREAS LANITIS
Keyword(s):  
3D Reconstruction ◽  
Feature Detection ◽  
Low Cost ◽  
Reconstruction Algorithms ◽  
3D Face Reconstruction ◽  
Reconstruction Process ◽  
3D Face ◽  
Advantages And Disadvantages ◽  
Face Reconstruction ◽  
Reconstruction Methods

The use of 3D data in face image processing applications has received considerable attention during the last few years. A major issue for the implementation of 3D face processing systems is the accurate and real time acquisition of 3D faces using low cost equipment. In this paper we provide a survey of 3D reconstruction methods used for generating the 3D appearance of a face using either a single or multiple 2D images captured with ordinary equipment such as digital cameras and camcorders. In this context we discuss various issues pertaining to the general problem of 3D face reconstruction such as the existence of suitable 3D face databases, correspondence of 3D faces, feature detection, deformable 3D models and typical assumptions used during the reconstruction process. Different approaches to the problem of 3D reconstruction are presented and for each category the most important advantages and disadvantages are outlined. In particular we describe example-based methods, stereo methods, video-based methods and silhouette-based methods. The issue of performance evaluation of 3D face reconstruction algorithms, the state of the art and future trends are also discussed.

Single-particle XFEL 3D reconstruction of ribosome-size particles based on Fourier slice matching: requirements to reach subnanometer resolution

2018 ◽  
Vol 25 (4) ◽  
pp. 1010-1021 ◽  
Author(s):  
Miki Nakano ◽  
Osamu Miyashita ◽  
Slavica Jonic ◽  
Atsushi Tokuhisa ◽  
Florence Tama
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
3D Models ◽  
Molecular Structures ◽  
Experimental Conditions ◽  
Beam Incidence ◽  
Minimum Requirements ◽  
Diffraction Patterns ◽  
Insight Into

Three-dimensional (3D) structures of biomolecules provide insight into their functions. Using X-ray free-electron laser (XFEL) scattering experiments, it was possible to observe biomolecules that are difficult to crystallize, under conditions that are similar to their natural environment. However, resolving 3D structure from XFEL data is not without its challenges. For example, strong beam intensity is required to obtain sufficient diffraction signal and the beam incidence angles to the molecule need to be estimated for diffraction patterns with significant noise. Therefore, it is important to quantitatively assess how the experimental conditions such as the amount of data and their quality affect the expected resolution of the resulting 3D models. In this study, as an example, the restoration of 3D structure of ribosome from two-dimensional diffraction patterns created by simulation is shown. Tests are performed using the diffraction patterns simulated for different beam intensities and using different numbers of these patterns. Guidelines for selecting parameters for slice-matching 3D reconstruction procedures are established. Also, the minimum requirements for XFEL experimental conditions to obtain diffraction patterns for reconstructing molecular structures to a high-resolution of a few nanometers are discussed.

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

Structural Studies on the Eukaryotic Ribosome

1990 ◽  
Vol 48 (1) ◽  
pp. 256-257
Author(s):  
Adriana Verschoor ◽  
Ronald Milligan ◽  
Suman Srivastava ◽  
Joachim Frank
Keyword(s):  
Chick Embryo ◽  
3D Reconstruction ◽  
Single Particle ◽  
Mass Density ◽  
Particle Surface ◽  
Uranyl Acetate ◽  
Electron Microscopic ◽  
Eukaryotic Ribosome ◽  
Negative Stain ◽  
Reconstruction Methods

We have studied the eukaryotic ribosome from two vertebrate species (rabbit reticulocyte and chick embryo ribosomes) in several different electron microscopic preparations (Fig. 1a-d), and we have applied image processing methods to two of the types of images. Reticulocyte ribosomes were examined in both negative stain (0.5% uranyl acetate, in a double-carbon preparation) and frozen hydrated preparation as single-particle specimens. In addition, chick embryo ribosomes in tetrameric and crystalline assemblies in frozen hydrated preparation have been examined. 2D averaging, multivariate statistical analysis, and classification methods have been applied to the negatively stained single-particle micrographs and the frozen hydrated tetramer micrographs to obtain statistically well defined projection images of the ribosome (Fig. 2a,c). 3D reconstruction methods, the random conical reconstruction scheme and weighted back projection, were applied to the negative-stain data, and several closely related reconstructions were obtained. The principal 3D reconstruction (Fig. 2b), which has a resolution of 3.7 nm according to the differential phase residual criterion, can be compared to the images of individual ribosomes in a 2D tetramer average (Fig. 2c) at a similar resolution, and a good agreement of the general morphology and of many of the characteristic features is seen.Both data sets show the ribosome in roughly the same ’view’ or orientation, with respect to the adsorptive surface in the electron microscopic preparation, as judged by the agreement in both the projected form and the distribution of characteristic density features. The negative-stain reconstruction reveals details of the ribosome morphology; the 2D frozen-hydrated average provides projection information on the native mass-density distribution within the structure. The 40S subunit appears to have an elongate core of higher density, while the 60S subunit shows a more complex pattern of dense features, comprising a rather globular core, locally extending close to the particle surface.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Sign in / Sign up

Export Citation Format

Share Document