New Method For 3D Reconstruction Research Articles

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.

Development and Validation of a Mobile Application for Measuring Tibial Torsion.

Tibial torsion lacks a single and reliable method for its measurement. While physical examination, computed tomography (CT), and EOS imaging are used complementarily, three-dimensional (3D) CT is the most widely used method for intuitive documentation and visualization. However, concern regarding the associated radiation hazard limits its use in the evaluation of pediatric patients. Moreover, EOS machines are too expensive and too large to be placed in every clinic requiring the measurement of tibial torsion. Therefore, a new method for 3D reconstruction is needed. In the present study, we tested the validity and reliability of a novel reconstruction tool for the lower leg. A statistical shape model and Laplacian constraint were adopted for the development of a new reconstruction tool for measuring tibial torsion. Tibial torsion measurements based on a 3D reconstruction application and CT images for 36 patients were evaluated for intraobserver and interobserver reliability. Tibial torsion measurements for 75 patients were compared for validation. A 3D reconstruction system for the lower leg was developed as a mobile application and was installed in a portable device for easy access in the clinical setting. In terms of interobserver reliability, the intraclass correlation coefficient among 3 clinicians was 0.896 (95% confidence interval [CI], 0.828 to 0.941). The correlation coefficient between tibial torsion measured with use of 3D CT and that measured with the mobile application was 0.865 (p < 0.001). The mobile application showed excellent reliability and validity for measuring tibial torsion. Concurrent utilization with mobile application for the femur allows visualization of the rotational profile of the leg without the need for CT or EOS. Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Multi-slice ptychographic imaging with multistage coarse-to-fine reconstruction.

The ability to image 3D samples with optical sectioning is essential for the study of tomographic morphology in material and biological sciences. However, it is often hampered by limitations of acquisition speed and equipment complexity when performing 3D volumetric imaging. Here, we propose, to the best of our knowledge, a new method for 3D reconstruction from a minimum of four intensity-only measurements. The complementary structured patterns provided by the digital micromirror device (DMD) irradiate the outermost layer of the sample to generate the corresponding diffraction intensities for recording, which enables rapid scanning of loaded patterns for fast acquisition. Our multistage reconstruction algorithm first extracts the overall coarse-grained information, and then iteratively optimizes the information of different layers to obtain fine features, thereby achieving high-resolution 3D tomography. The high-fidelity reconstruction in experiments on two-slice resolution targets, unstained Polyrhachis vicina Roger and freely moving C. elegans proves the robustness of the method. Compared with traditional 3D reconstruction methods such as interferometry-based methods or Fourier ptychographic tomography (FPT), our method increases the reconstruction speed by at least 10 times and is suitable for label-free dynamic imaging in multiple-scattering samples. Such 3D reconstruction suggests potential applications in a wide range of fields.

Three-dimensional reconstruction of Kambin's triangle based on automated magnetic resonance image segmentation.

The three-dimensional (3D) anatomy of Kambin's triangle is crucial for surgical planning in minimally invasive spine surgery via the transforaminal approach. Few pieces of research have, however, used image segmentation to explore the 3D reconstruction of Kambin's triangle. This study aimed to develop a new method of 3D reconstruction of Kambin's triangle based on automated magnetic resonance image (MRI) segmentation of the lumbar spinal structures. An experienced (>5 years) "ground truth" spinal pain physician meticulously segmented and labeled spinal structures (e.g., bones, dura mater, discs, and nerve roots) on MRI. Subsequently, a 3D U-Net algorithm was developed for automatically segmenting lumbar spinal structures for the 3D reconstruction of Kambin's triangle. The Dice similarity coefficient (DSC), precision, recall, and the area of Kambin's triangle were used to assess anatomical performance. The automatic segmentation of all spinal structures at the L4/L5 levels and L5/S1 levels resulted in good performance: DSC = 0.878/0.883, precision = 0.889/0.890, recall = 0.873/0.882. Furthermore, the area measurements of Kambin's triangle revealed no significant difference between ground truth and automatic segmentation (p = 0.333 at the L4/L5 level, p = 0.302 at the L5/S1 level). The 3D U-Net model used in this study performed well in terms of simultaneous segmentation of multi-class spinal structures (including bones, dura mater, discs, and nerve roots) on MRI, allowing for accurate 3D reconstruction of Kambin's triangle.

Exploring the mechanical and morphological rationality of tree branch structure based on 3D point cloud analysis and the finite element method

Trees are thought to have acquired a mechanically optimized shape through evolution, but a scientific methodology to investigate the mechanical rationality of tree morphology remains to be established. The aim of this study was to develop a new method for 3D reconstruction of actual tree shape and to establish a theoretical formulation for elucidating the structure and function of tree branches. We obtained 3D point cloud data of tree shape of Japanese zelkova (Zelkova serrata) and Japanese larch (Larix kaempferi) using the NavVis Lidar scanner, then applied a cylinder structure extraction from point cloud data with error estimation. We then formulated the mechanical stress of branches under gravity using the elastic theory, and performed finite element method simulations to evaluate the mechanical characteristics. Subsequently, we constructed a mechanics-based theoretical formulation of branch development that ensures constant bending stress produces various branching patterns depending on growth properties. The derived theory recapitulates the trade-off among branch growth anisotropy, stress-gravity length, and branch shape, which may open the quantitative way to evaluate mechanical and morphological rationality of tree branches.

A 3D reconstruction method for buildings based on monocular vision

AbstractThe three‐dimensional (3D) reconstruction technology based on computer vision has greatly facilitated damage inspection and assessment and construction monitoring of civil engineering. However, there are several problems in its implementation. A new method of 3D reconstruction is hereby proposed in this paper regarding the geometrical characteristics of pictures of buildings. An algorithm can normalize the 3D‐reconstructed point cloud model so that its length, width, and height are parallel to the coordinate axis of the world coordinate system, and the absolute scale of the point cloud model can be obtained. Compared with the traditional one, this methodology can maintain better accuracy. This paper establishes the theoretical framework of the methodology, and steps for implementation are given by using digital image processing technology. After analyzing the results of a field experiment, it has been proven that this methodology can make the best use of the geometric features and improve the efficiency of the traditional reconstruction algorithm and therefore result in high accuracy.

A 3D reconstruction method of image sequence based on deep learning

Camera calibration, image feature detection, matching and other aspects have become barriers that traditional 3D reconstruction methods are difficult to break through. The important role of deep learning in data detection and classification has a deep impact on the real world, and has become a research hotspot at home and abroad to deal with this problem. In this paper, a 3D reconstruction method of image sequence based on depth learning is proposed. Firstly, the principle and method of 3D reconstruction are introduced. Then, the new method of 3D reconstruction based on image is studied and discussed in combination with deep learning theory. Finally, the conclusion and prospect are given.

3D Mapping and 6D Pose Computation for Real Time Augmented Reality on Cylindrical Objects

Visual Augmented Reality (AR) typically overlays virtual computer graphics or other virtual contents on the real world videos, attracting much interest from both academic and industrial communities. Although AR techniques on planes are well studied, cylindrical objects are seldom used for augmented reality. In this paper, we propose a new method for 3D reconstruction and 6D pose computation for augmented reality on a cylindrical object. The 6D pose is the relative pose between the camera and the cylindrical object, which is very convenient to make augmented reality. First, we capture some images with a cylindrical object and then reconstruct its 3D model with textures offline by using projective invariance and image contours. Second, according to the 3D model, we track the 6D relative pose between the camera and the cylindrical object online, where we propose a linear P3P RANSAC to remove outliers. Finally, the virtual images are exactly aligned with the cylindrical object in the real world. Experimental results show that the proposed method outperforms the state of the arts in terms of 3D mapping and 6D pose computation on cylindrical objects.

Development and Validation of a Mobile Application for Measuring Femoral Anteversion in Patients With Cerebral Palsy.

Computed tomography (CT) provides benefits for 3-dimensional (3D) visualization of femur deformities. However, the potential adverse effects of radiation exposure have become a concern. Consequently, a biplanar imaging system EOS has been proposed to enable reconstruction of the 3D model of the femur. However, this system requires a calibrated apparatus, the cost of which is high, and the area occupied by it is substantial. The purpose of this study was to develop a mobile application that included a new method of 3D reconstruction of the femur from conventional radiographic images and to evaluate the validity and reliability of mobile the application when measuring femoral anteversion. The statistical shape model, graph-cut algorithm, and iterative Perspective-n-Point algorithm were utilized to develop the application. The anteroposterior and lateral images of a femur can be input using the embedded camera or by file transfer, and the touch interface aids accurate contouring of the femur. Regarding validation, the CT scans and conventional radiographic images of 36 patients with cerebral palsy were used. To evaluate concurrent validity, the femoral anteversion measurements on the images reconstructed from the mobile application were compared with those from the 3D CT images. Three clinicians assessed interobserver reliability. The mobile application, which reconstructs the 3D image from conventional radiographs, was successfully developed. Regarding concurrent validity, the correlation coefficient between femoral anteversion measured using 3D CT and the mobile application was 0.968 (P<0.001). In terms of interobserver reliability, the intraclass correlation coefficient among the 3 clinicians was 0.953. The measurement of femoral anteversion with the mobile application showed excellent concurrent validity and reliability in patients with cerebral palsy. The proposed mobile application can be used with conventional radiographs and does not require additional apparatus. It can be used as a convenient technique in hospitals that cannot afford a CT machine or an EOS system. Level III-diagnostic.

3D skin surface reconstruction from a single image by merging global curvature and local texture using the guided filtering for 3D haptic palpation.

Skin surface evaluation has been studied using various imaging techniques. However, all these studies had limited impact because they were performed using visual exam only. To improve on this scenario with haptic feedback, we propose 3D reconstruction of the skin surface using a single image. Unlike extant 3D skin surface reconstruction algorithms, we utilize the local texture and global curvature regions, combining the results for reconstruction. The first entails the reconstruction of global curvature, achieved by bilateral filtering that removes noise on the surface while maintaining the edge (ie, furrow) to obtain the overall curvature. The second entails the reconstruction of local texture, representing the fine wrinkles of the skin, using an advanced form of bilateral filtering. The final image is then composed by merging the two reconstructed images. We tested the curvature reconstruction part by comparing the resulting curvatures with measured values from real phantom objects while local texture reconstruction was verified by measuring skin surface roughness. Then, we showed the reconstructed result of our proposed algorithm via the reconstruction of various real skin surfaces. The experimental results demonstrate that our approach is a promising technology to reconstruct an accurate skin surface with a single skin image. We proposed 3D skin surface reconstruction using only a single camera. We highlighted the utility of global curvature, which has not been considered important in the past. Thus, we proposed a new method for 3D reconstruction that can be used for 3D haptic palpation, dividing the concepts of local and global regions.

Fast and Easy 3D Reconstruction with the Help of Geometric Constraints and Genetic Algorithms

The purpose of the work presented in this paper is to describe new method of 3D reconstruction from one or more uncalibrated images. This method is based on two important concepts: geometric constraints and genetic algorithms (GAs). At first, we are going to discuss the combination between bundle adjustment and GAs that we have proposed in order to improve 3D reconstruction efficiency and success. We used GAs in order to improve fitness quality of initial values that are used in the optimization problem. It will increase surely convergence rate. Extracted geometric constraints are used first to obtain an estimated value of focal length that helps us in the initialization step. Matching homologous points and constraints is used to estimate the 3D model. In fact, our new method gives us a lot of advantages: reducing the estimated parameter number in optimization step, decreasing used image number, winning time and stabilizing good quality of 3D results. At the end, without any prior information about our 3D scene, we obtain an accurate calibration of the cameras, and a realistic 3D model that strictly respects the geometric constraints defined before in an easy way. Various data and examples will be used to highlight the efficiency and competitiveness of our present approach.

A New Method for 3D Reconstruction of Headlight Contour

This paper put forward a 3D reconstruction method of the headlight contours based on laser scanning technology and robotics. Firstly, according to the present three-dimensional measurement techniques, the article put forward a set of headlight contour detection method based on the analytic geometry model and the line laser source scanning principle. It establishes a 3D scanning model and coordinate transformation model for 3D reconstruction of the headlight contour. Secondly, according to the demanding accuracy it structures the 3D reconstruction system. Finally it realizes the 3D reconstruction of the headlight contour based on the method, and the result is tested and evaluated matching effect, the result shows that can effectively realize the 3D reconstruction of headlight contour and the method has a good stability.

New method for 3D reconstruction of the human cranial vault from CT-scan data

This study presents a new method for the 3D reconstruction of the human cranial vault from routine Computed Tomography (CT) data. The reconstruction method was based on the conceptualization of the shape of the cranial vault with a parametric description. An initialization was first realized with the identification of anatomical landmarks and contours on Digitally Reconstructed Radiographs (DRR) in order to obtain a pre-personalized reconstruction. Then an optimization of the reconstruction was performed to segment the internal and external surfaces of the cranial vault for thickness computation. The method was validated by comparing final reconstructions issued from our approach and from a manual slice-by-slice segmentation method on ten CT-scans. Errors were comparable to the CT image resolution, and less than 2 min were dedicated to the operatordependant marking step. The reconstruction of internal and external surfaces of the cranial vault allows quantifying and visualizing of thickness throughout the cranial vault. This thickness mapping is useful for clinical purposes as additional pre-surgical information. Moreover, this study constitutes a first step in the personalized characterization of skull resistance directly from routine exams.

3D Reconstruction from Orthographic Image and Optimization Processing of NC Machining for Reconstructed Part

For inverse constructing object from its orthographic image, a new method of 3D reconstruction based on image vectorization and bi-arc approximation of cubic B-spline is investigated. A 3D CAD model is automatically acquired from 2D views by means of 2D primitive recognition, 3D reconstruction of primitive and combination of 3D primitive bodies. With the help of CimatronE8.0 software, reasonable procedure design and optimization toolpath planning are applied to increase machining accuracy and productivity during automatic programming for reconstructed 3D solid. Reconstructed parts are machined on the VMC-600 type machine center with FANUC system. The results of application indicate that this method has many advantages, such as quickly obtaining the CAD model of part, getting smooth machined surface, efficient machining without invalid or reduplicate feed and so on. As a result, it can be applied widely in NC machining field.

Multi-stage 3D reconstruction under circular motion

In this paper, we propose a new method for 3D reconstruction from an image sequence captured by a camera with constant intrinsic parameters undergoing circular motion. We introduce a method, called circular projective reconstruction, for enforcing the circular constraint in a factorization-based projective reconstruction. To deal with the missing data problem, our method uses a multi-stage approach to reconstructing the objects and cameras, which first computes a circular projective reconstruction of a sub-sequence and then extends the reconstruction to the complete sequence. Camera matrix, rotation angles, and 3D structure are computed iteratively in a way that the 2D reprojection error is minimized. The algorithm is evaluated using real image sequences.

Segmentation and three-dimension reconstruction of Chinese digitized human cerebrum

Objective A 3D digitized visible model of human cerebrum was built to provide anatomical structure for making plans of cerebral surgical operation and realizing accurate simulation of cerebrum on computer. Methods Transverse sectional anatomy data of the cerebrum were chosen from the first Chinese visible human (one male and one female). Semi-automated segmentation and Photoshop software were selected to segment cerebral cortex, white matter, basal nuclei, lateral ventricle, hippocampus, etc. On personal computer, the segmented structures were reconstructed in 3D with volume rendering reconstruction and surface rendering reconstruction. Results Two accurately segmented images of the main structures of cerebrum were completed. The reconstructed structures can be displayed singly, in small groups or as a whole and can be continuously rotated in 3D space at different velocities. Conclusion Combining volume-rendering reconstruction with surface rendering reconstruction overcomes the defects of surface rendering reconstruction that lack of internal anatomical information, which provides a new method for 3D reconstruction. The reconstructed cerebrum and the main internal structures are realistic, which demonstrates the natural shape and exact position of the structures. It provides an accurate model for the automated segmentation algorithmic study and provides a digitized anatomical mode of cerebrum.

Electron Spectroscopic Tomography for Materials Science

Tomographic 3D-reconstructions from tilt series of projections differ in two major aspects when moving from biochemistry (e.g.) to materials science applications: (I) Crystalline inorganic materials fail the weak phase image formation approximation and suffer from dynamic and nonlinear contrast contributions (which depend for both CTEM and HREM on atomic lattice plane orientation). (II) exposure tolerance increases by two orders of magnitude. We therefore propose energy filtered tomography with inelastic electrons (electron spectroscopic imaging, ESI) as a new method for 3D-reconstruction of free or buried subsurface nanostructures or nanoparticles. Energy filters are not used here to improve SNR by removing inelastic background , but to provide the necessary projection relationship between a 3D concentration distribution and the (processed) image intensity. Ideally, a 4-dimensional data space is recorded for a single tilt axis tomography experiment, D=D(δE, Θ,x,y), (with 5E as energy loss, Θ as tilt, and (x,y) as image coordinates). The material has to withstand exposures of T = t × Ntilt × Nloss (t = 5...20sec per image, Ntilt &gt;12 directions, Nloss = 5....20 energy loss values).

External marker-based automatic congruencing: a new method of 3D reconstruction from serial sections.

Computer-based three-dimensional (3D) visualizations reconstructed from sectional images represent a valuable tool in biomedical research and medical diagnosis. Particularly with those imaging techniques that provide virtual sections, such as CT, MRI, and CLSM, 3D reconstructions have become routine. Reconstructions from physical sections, such as those used in histological preparations, have not experienced an equivalent breakthrough, due to inherent shortcomings in sectional preparation that impede automated image-processing and reconstruction. The increased use of molecular techniques in morphological research, however, generates an overwhelming amount of 3D molecular information, stored within series of physical sections. This valuable information can be fully appreciated and interpreted only through an adequate method of 3D visualization. In this paper we present a new method for a reliable and largely automated 3D reconstruction from physically sectioned material. The 'EMAC' concept (External Marker-based Automatic Congruencing) successfully approaches the three major obstacles to automated 3D reconstruction from serial physical sections: misalignment, distortion, and staining variation. It utilizes the objectivity of external markers for realignment of the sectional images and for geometric correction of distortion. A self-adapting dynamic thresholding technique compensates for artifactual staining variation and automatically selects the desired object contours. Implemented on a low-cost hardware platform, EMAC provides a fast and efficient tool that largely facilitates the use of computer-based 3D visualization for the analysis of complex structural, molecular, and genetic information in morphological research. Due to its conceptual versatility, EMAC can be easily adapted for a broad range of tasks, including all modern molecular-staining techniques, such as immunohistochemistry and in situ hybridization.

External marker‐based automatic congruencing: A new method of 3D reconstruction from serial sections

Background Computer-based three-dimensional (3D) visualizations reconstructed from sectional images represent a valuable tool in biomedical research and medical diagnosis. Particularly with those imaging techniques that provide virtual sections, such as CT, MRI, and CLSM, 3D reconstructions have become routine. Reconstructions from physical sections, such as those used in histological preparations, have not experienced an equivalent breakthrough, due to inherent shortcomings in sectional preparation that impede automated image-processing and reconstruction. The increased use of molecular techniques in morphological research, however, generates an overwhelming amount of 3D molecular information, stored within series of physical sections. This valuable information can be fully appreciated and interpreted only through an adequate method of 3D visualization. Methods and results:In this paper we present a new method for a reliable and largely automated 3D reconstruction from physically sectioned material. The ‘EMAC‘ concept (External Marker-based Automatic Congruencing) successfully approaches the three major obstacles to automated 3D reconstruction from serial physical sections: misalignment, distortion, and staining variation. It utilizes the objectivity of external markers for realignment of the sectional images and for geometric correction of distortion. A self-adapting dynamic thresholding technique compensates for artifactual staining variation and automatically selects the desired object contours. Conclusions Implemented on a low-cost hardware platform, EMAC provides a fast and efficient tool that largely facilitates the use of computer-based 3D visualization for the analysis of complex structural, molecular, and genetic information in morphological research. Due to its conceptual versatility, EMAC can be easily adapted for a broad range of tasks, including all modern molecular-staining techniques, such as immunohistochemistry and in situ hybridization. Anat. Rec. 248:583-602, 1997. © 1997 Wiley-Liss, Inc.

Accurate Three-Dimensional Reconstruction of Intravascular Ultrasound Data

The geometrical accuracy of conventional three-dimensional (3D) reconstruction methods for intravascular ultrasound (IVUS) data (coronary and peripheral) is hampered by the inability to register spatial image orientation and by respiratory and cardiac motion. The objective of this work was the development of improved IVUS reconstruction techniques. We developed a 3D position registration method that identifies the spatial coordinates of an in situ IVUS catheter by use of simultaneous ECG-gated biplane digital cinefluoroscopy. To minimize distortion, coordinates underwent pincushion correction and were referenced to a standardized calibration cube. Gated IVUS data were acquired digitally, and the spatial locations of the imaging planes were then transformed relative to their respective 3D coordinates, rendered in binary voxel format, resliced, and displayed on an image-processing workstation for off-line analysis. The method was tested by use of phantoms (straight tube, 360 degrees circle, 240 degrees spiral) and an in vitro coronary artery model. In vivo feasibility was assessed in patients who underwent routine interventional coronary procedures accompanied by IVUS evaluation. Actual versus calculated point locations were within 1.0 +/- 0.3 mm of each other (n = 39). Calculated phantom volumes were within 4% of actual volumes. Phantom 3D reconstruction appropriately demonstrated complex morphology. Initial patient evaluation demonstrated method feasibility as well as errors if respiratory and ECG gating were not used. These preliminary data support the use of this new method of 3D reconstruction of vascular structures with use of combined vascular ultrasound data and simultaneous ECG-gated biplane cinefluoroscopy.