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.

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 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 Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

2019 ◽  
Vol 11 (10) ◽  
pp. 1204 ◽  
Author(s):  
Yue Pan ◽  
Yiqing Dong ◽  
Dalei Wang ◽  
Airong Chen ◽  
Zhen Ye
Keyword(s):  
Classification Tree ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Surface Model ◽  
Reconstruction Method ◽  
3D Point Clouds ◽  
Surface Models ◽  
Structural Surface ◽  
Better Than

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

scholarly journals Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ryuhei Ando ◽  
Yuko Ozasa ◽  
Wei Guo
Keyword(s):  
Surface Reconstruction ◽  
Leaf Surface ◽  
Imaging Techniques ◽  
Point Clouds ◽  
Plant Phenotyping ◽  
Reconstruction Method ◽  
Trade Off ◽  
3D Point Clouds ◽  
Leaf Surfaces ◽  
Conventional Methods

The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

scholarly journals Evaluation of feature-based methods for automated network orientation

2014 ◽  
Vol XL-5 ◽  
pp. 47-54 ◽  
Author(s):  
F.I. Apollonio ◽  
A. Ballabeni ◽  
M. Gaiani ◽  
F. Remondino
Keyword(s):  
Image Processing ◽  
3D Reconstruction ◽  
Processing Time ◽  
Point Clouds ◽  
Large Networks ◽  
Sift Algorithm ◽  
3D Point Clouds ◽  
Network Orientation ◽  
Feature Based ◽  
Automated Image Processing

Every day new tools and algorithms for automated image processing and 3D reconstruction purposes become available, giving the possibility to process large networks of unoriented and markerless images, delivering sparse 3D point clouds at reasonable processing time. In this paper we evaluate some feature-based methods used to automatically extract the tie points necessary for calibration and orientation procedures, in order to better understand their performances for 3D reconstruction purposes. The performed tests – based on the analysis of the SIFT algorithm and its most used variants – processed some datasets and analysed various interesting parameters and outcomes (e.g. number of oriented cameras, average rays per 3D points, average intersection angles per 3D points, theoretical precision of the computed 3D object coordinates, etc.).

scholarly journals GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

2020 ◽  
Vol 12 (3) ◽  
pp. 351 ◽  
Author(s):  
Seyyed Meghdad Hasheminasab ◽  
Tian Zhou ◽  
Ayman Habib
Keyword(s):  
3D Reconstruction ◽  
Structure From Motion ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Agricultural Fields ◽  
System Calibration ◽  
3D Point Clouds ◽  
Digital Agriculture ◽  
Orientation Parameters ◽  
Calibration Parameters

Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.

scholarly journals Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

2020 ◽  
Vol 10 (5) ◽  
pp. 1744 ◽  
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Hough Transform ◽  
Detection System ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Polar Coordinates ◽  
Processing Unit ◽  
3D Point Clouds ◽  
Hough Space ◽  
Plane Extraction ◽  
3D Hough Transform

Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.

scholarly journals Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 664
Author(s):  
Zhihong Ma ◽  
Dawei Sun ◽  
Haixia Xu ◽  
Yueming Zhu ◽  
Yong He ◽  
...  
Keyword(s):  
Stress Responses ◽  
Average Distance ◽  
3D Structure ◽  
Three Dimensional ◽  
Time Of Flight ◽  
Bilateral Filter ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Plant Data ◽  
Tof Camera

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2. A K-dimension (k-d) tree was applied to spatial topological relationships for searching points. Background noise points were then removed with a minimum oriented bounding box (MOBB) with a pass-through filter, while outliers and flying pixel points were removed based on viewpoints and surface normals. After being smoothed with the bilateral filter, the 3D plant data were registered and meshed. We adjusted the mesh patches to eliminate layered points. The results showed that the patches were closer. The average distance between the patches was 1.88 × 10−3 m, and the average angle was 17.64°, which were 54.97% and 48.33% of those values before optimization. The proposed method performed better in reducing noise and the local layered-points phenomenon, and it could help to more accurately determine 3D structure parameters from point clouds and mesh models.

scholarly journals Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants

2021 ◽  
Vol 13 (11) ◽  
pp. 2113
Author(s):  
Tian Gao ◽  
Feiyu Zhu ◽  
Puneet Paul ◽  
Jaspreet Sandhu ◽  
Henry Akrofi Doku ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Throughput ◽  
Imaging System ◽  
Point Clouds ◽  
Imaging Systems ◽  
Phenotypic Traits ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
3D Point Clouds ◽  
High Throughput Phenotyping

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

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