scholarly journals Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts

Buildings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Siyu Han ◽  
Linsheng Huo ◽  
Yize Wang ◽  
Jing Zhou ◽  
Hongnan Li
Keyword(s):  
3D Reconstruction ◽  
Traditional Method ◽  
Structural Model ◽  
Point Clouds ◽  
Graph Cuts ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Modeling Accuracy ◽  
City Construction ◽  
Emergency Scenarios

The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its application in emergency scenarios. In this paper, a rapid reconstruction method is proposed which combines the traditional image-based 3D reconstruction technique and an interactive graph cuts algorithm. Firstly, a sequence of images is collected around the target structure. Then, the images are preprocessed using the interactive iterative graph cuts algorithm to extract the target from each image. Finally, the resulting sequence of images is used to perform the 3D reconstruction. During the preprocessing, only a few images require manual intervention while the rest can be processed automatically. To verify the modeling accuracy of the proposed method, a column that has been destroyed is selected as a target for 3D reconstruction. The results show that compared with the traditional method, the modeling efficiency of the fast reconstruction method is doubled. In addition, the modeling accuracy is 97.65%, which is comparable to the modeling accuracy of the traditional method (97.73%); as well, by comparing the point clouds, the alignment between the two models is tremendously close, with tiny difference. The proposed rapid reconstruction method can be applied in emergency scenarios, such as rapid assessment in post-disaster situations.

CROWD-SOURCED VISUAL DATA COLLECTION FOR MONITORING INDOOR CONSTRUCTION IN 3D

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fouad Amer ◽  
Mani Golparvar-Fard
Keyword(s):  
Data Collection ◽  
3D Reconstruction ◽  
Structure From Motion ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Visual Data ◽  
Mapping Algorithm ◽  
3D Point Clouds ◽  
Localization And Mapping

Complete and accurate 3D monitoring of indoor construction progress using visual data is challenging. It requires (a) capturing a large number of overlapping images, which is time-consuming and labor-intensive to collect, and (b) processing using Structure from Motion (SfM) algorithms, which can be computationally expensive. To address these inefficiencies, this paper proposes a hybrid SfM-SLAM 3D reconstruction algorithm along with a decentralized data collection workflow to map indoor construction work locations in 3D and any desired frequency. The hybrid 3D reconstruction method is composed of a pipeline of Structure from Motion (SfM) coupled with Multi-View Stereo (MVS) to generate 3D point clouds and a SLAM (Simultaneous Localization and Mapping) algorithm to register the separately formed models together. Our SfM and SLAM pipelines are built on binary Oriented FAST and Rotated BRIEF (ORB) descriptors to tightly couple these two separate reconstruction workflows and enable fast computation. To elaborate the data capture workflow and validate the proposed method, a case study was conducted on a real-world construction site. Compared to state-of-the-art methods, our preliminary results show a decrease in both registration error and processing time, demonstrating the potential of using daily images captured by different trades coupled with weekly walkthrough videos captured by a field engineer for complete 3D visual monitoring of indoor construction operations.

scholarly journals Diagnostic value and forensic relevance of a novel photorealistic 3D reconstruction technique in post-mortem CT

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200204
Author(s):  
Judith Böven ◽  
Johannes Boos ◽  
Andrea Steuwe ◽  
Janna Morawitz ◽  
Lino Morris Sawicki ◽  
...  
Keyword(s):  
Soft Tissue ◽  
3D Reconstruction ◽  
Foreign Bodies ◽  
Soft Tissue Injuries ◽  
Fracture Dislocation ◽  
Whole Body ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Post Mortem ◽  
Tissue Injuries

Objectives: Evaluation of performance and forensic relevance of a novel, photorealistic, 3D reconstruction method (cinematic rendering, (CR)) in comparison with conventional post-mortem CT (PMCT) and volume rendering (VR) technique for visualization of traumatic injuries. Methods: 112 pathologies (fractures, soft tissue injuries and foreign bodies) from 33 human cadavers undergoing whole body PMCT after traumatic death were retrospectively analyzed. Pathologies were reconstructed with CR and VR techniques. Fractures were classified according to their dislocation. Images were evaluated according to their expressiveness and judicial relevance by two forensic pathologists using a five-level Likert-scale (1: high expressiveness, 5: low expressiveness). They decided whether CR reconstructions were suitable for judicial reviews. The detection rate of pathologies was determined by two radiologists. Results: CR was more expressive than VR for all three trauma categories (p < 0.01) and than conventional CT when used for fractures with dislocation (p < 0.001), injuries of the ventral body surface (p < 0.001), and demonstration of foreign bodies (p = 0.033). CR and VR became more expressive with a higher grade of fracture dislocation (p < 0.001). 20% of all pathologies in the CR and VR reconstructions were not detectable by radiologists. Conclusion: CR reconstructions are superior to VR regarding the expressiveness. For fractures with substantial dislocation, soft tissue injuries, and foreign bodies in situ, CR showed a significantly better expressiveness than conventional PMCT. CR and VR have significant limitations in cases of fractures with minor dislocations and covered soft tissue injuries. Advances in knowledge: CR is a helpful tool to present pathologies found in PMCT for judicial reviews.

scholarly journals Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4628
Author(s):  
Xiaowen Teng ◽  
Guangsheng Zhou ◽  
Yuxuan Wu ◽  
Chenglong Huang ◽  
Wanjing Dong ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Depth Image ◽  
Scanning System ◽  
Reconstruction Method ◽  
Three Dimensional Reconstruction ◽  
Hardware Cost ◽  
Dimensional Reconstruction

The three-dimensional reconstruction method using RGB-D camera has a good balance in hardware cost and point cloud quality. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems. Shoot color images, depth images and near-infrared images of the target from six perspectives by Azure Kinect sensor with black background. Multiply the binarization result of the 8-bit infrared image with the RGB-D image alignment result provided by Microsoft corporation, which can remove ghosting and most of the background noise. A neighborhood extreme filtering method is proposed to filter out the abrupt points in the depth image, by which the floating noise point and most of the outlier noise will be removed before generating the point cloud, and then using the pass-through filter eliminate rest of the outlier noise. An improved method based on the classic iterative closest point (ICP) algorithm is presented to merge multiple-views point clouds. By continuously reducing both the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the integral color point cloud. Many experiments on rapeseed plants show that the success rate of cloud registration is 92.5% and the point cloud accuracy obtained by this method is 0.789 mm, the time consuming of a integral scanning is 302 seconds, and with a good color restoration. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower when building a automatic scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of rapeseed and other crops phenotype.

scholarly journals 3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

2021 ◽  
Author(s):  
Xiaowen Teng ◽  
Guangsheng Zhou ◽  
Yuxuan Wu ◽  
Chenglong Huang ◽  
Wanjing Dong ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Near Infrared ◽  
Infrared Image ◽  
Depth Map ◽  
Point Clouds ◽  
Scanning System ◽  
Reconstruction Method ◽  
Hardware Cost ◽  
Scan Time

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

3D Surface Reconstruction from Single 2D Image

2012 ◽  
Vol 522 ◽  
pp. 886-890 ◽  
Author(s):  
Feng He Wu ◽  
Jin Fen Wang ◽  
Jun Wang ◽  
Yu Bo Ren
Keyword(s):  
3D Reconstruction ◽  
Surface Reconstruction ◽  
Color Image ◽  
Spline Interpolation ◽  
Shape From Shading ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
B Spline ◽  
3D Surface Reconstruction ◽  
3D Surface

The image-based 3D reconstruction technique is an important method of 3D data passive acquired in computer vision. Aimed at the limitation of traditional SFS based 3D surface reconstruction method, a combined image-based 3D surface reconstruction method is given in this paper. Firstly, the pretreatment method of gradation is applied to convert the input color image to 256 gray image; secondly, shape from shading method is applied to recover the 3D profile of object; finally, the B-spline technique is adopted to reconstruct the model of surface and improve the resolution of surface. The key techniques such as the principle and algorithm of B-spline interpolation reconstruction are analyzed, and the experimental results are also given. The single-image based 3D reconstruction method given in this paper will supply a helpful condition for the extensive used of SFS technique.

scholarly journals 3D Reconstruction with circular light field by using 3D hough transformation

2021 ◽  
Vol 39 (1) ◽  
pp. 135-140
Author(s):  
Zhengxi Song ◽  
Qi Wu ◽  
Xue Wang ◽  
Qing Wang
Keyword(s):  
3D Reconstruction ◽  
Light Field ◽  
3D Structure ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Hough Transformation ◽  
3D Point Clouds ◽  
Hough Space ◽  
Reconstruction Performance ◽  
Image Volume

Aiming at the issue of incomplete trajectories in the 2D epipolar image of circular light field, this paper proposes a 3D reconstruction method by using 3D Hough transformation. This method computes 3D point clouds by computing the parameters of feature trajectories in 3D image volume. By analyzing the 3D distribution of circular light field trajectories, binary curves in image volume are extracted, and their local orientation are further estimated by the 3D structure tensor. The 3D Hough space generation and the parameter selection method are proposed to the 3D curves detection. The parameters of these curves are converted to 3D point clouds on each view and then merged to final 3D reconstruction. The ambiguity of Hough transformation solution on 2D epipolar image is overcome by the 3D analyzing method. The experiments are carried out on both synthetic and real datasets. The experiment results show that this method can improve the reconstruction performance compared with the state-of-the-art in circular light field.

Alternative pulmonary artery reconstruction technique in the arterial switch operation

2021 ◽  
Author(s):  
Hisayuki Hongu ◽  
Masaaki Yamagishi ◽  
Yoshinobu Maeda ◽  
Keiichi Itatani ◽  
Masatoshi Shimada ◽  
...  
Keyword(s):  
Pulmonary Artery ◽  
Arterial Switch Operation ◽  
Late Complication ◽  
Late Complications ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Arterial Switch ◽  
Switch Operation ◽  
Longitudinal Extension

Abstract OBJECTIVES Late complications of arterial switch operations (ASO) for transposition of the great arteries, such as neo-pulmonary artery (PA) stenosis and/or neoaortic regurgitation, have been reported. We developed an alternative reconstruction method called the longitudinal extension (LE) method to prevent PA bifurcation stenosis (PABS). METHODS We identified 48 patients diagnosed with transposition of the great arteries and performed ASO using the Lecompte manoeuvre for neo-PA reconstruction. In 9 consecutive patients (from 2014), the LE method was performed (LE). Before 2014, conventional techniques were performed in 39 patients (C). The median body weight and age in the LE and C groups were 3.0 and 3.1 kg and 12 and 26 days, respectively. In the LE group, 1 patient underwent bilateral PA banding before ASO. In C, PA banding and arch repair were performed in 1 patient each. Patients who received concomitant procedures were included. RESULTS The median follow-up in LE and C groups was 1.9 and 10.1 years, respectively. Early mortality/late death was not found in group LE and in 1 patient in group C. Only 1 case required ascending aorta sliding plasty in LE, and 8 patients needed PA augmentation for PABS in C. The median velocity of right/left PA was measured as 1.6/1.9 m/s in LE and 2.1/2.3 m/s in C, so it showed a lower value in LE. CONCLUSIONS Excellent mid-term results were obtained with the LE method. It was considered a useful procedure in preventing PABS, which is a primary late complication of ASO. Further follow-up and investigations are needed.

scholarly journals Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

2021 ◽  
Vol 13 (9) ◽  
pp. 1859
Author(s):  
Xiangyang Liu ◽  
Yaxiong Wang ◽  
Feng Kang ◽  
Yang Yue ◽  
Yongjun Zheng
Keyword(s):  
Convex Hull ◽  
Clustering Algorithm ◽  
Plant Protection ◽  
Point Clouds ◽  
Biomass Estimation ◽  
Reconstruction Method ◽  
Volume Calculation ◽  
Alpha Shape ◽  
3D Point Clouds ◽  
Canopy Volume

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation.

scholarly journals 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Structured Light ◽  
Color Space ◽  
Reconstruction Technique ◽  
Single Shot ◽  
Line Pattern ◽  
Hsv Color Space ◽  
Phase Map ◽  
Line Segmentation

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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