Search Results (279)

Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (Lentinula edodes) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding R² > 0.8 and nRMSE < 0.09 for the pileus transverse and longitudinal diameters, R² = 0.53 and RMSE = 3.26 mm for the pileus height, R² = 0.79 and nRMSE = 0.12 for stipe diameter, and R² = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (R² = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives. Full article

The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications. Full article

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R² values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture. Full article

The maize tassel represents one of the most pivotal organs dictating maize yield and quality. Investigating its phenotypic information constitutes an exceedingly crucial task within the realm of breeding work, given that an optimal tassel structure is fundamental for attaining high maize yields. High-throughput phenotyping technologies furnish significant tools to augment the efficiency of analyzing maize tassel phenotypic information. Towards this end, we engineered a fully automated multi-angle digital imaging apparatus dedicated to maize tassels. This device was employed to capture images of tassels from 1227 inbred maize lines falling under three genotype classifications (NSS, TST, and SS). By leveraging the 3D reconstruction algorithm SFM (Structure from Motion), we promptly obtained point clouds of the maize tassels. Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. These encompassed main spike diameter, crown height, main spike length, stem length, stem diameter, the number of branches, total branch length, average crown diameter, maximum crown diameter, convex hull volume, and crown area. Finally, we compared the GFC (Gaussian Fuzzy Clustering algorithm) used in this study with commonly used algorithms, such as RF (Random Forest), SVM (Support Vector Machine), and BPNN (BP Neural Network), as well as k-Means, HCM (Hierarchical), and FCM (Fuzzy C-Means). We then conducted a correlation analysis between the extracted phenotypic parameters of the maize tassel structure and the genotypes of the maize materials. The research results showed that the Gaussian Fuzzy Clustering algorithm was the optimal choice for clustering maize genotypes. Specifically, its classification accuracies for the Non-Stiff Stalk (NSS) genotype and the Tropical and Subtropical (TST) genotype reached 67.7% and 78.5%, respectively. Moreover, among the materials with different maize genotypes, the number of branches, the total branch length, and the main spike length were the three indicators with the highest variability, while the crown volume, the average crown diameter, and the crown area were the three indicators with the lowest variability. This not only provided an important reference for the in-depth exploration of the variability of the phenotypic parameters of maize tassels but also opened up a new approach for screening breeding materials. Full article

Mapping constitutes a critical component of architectural heritage research, providing the groundwork for both conservation and utilization efforts. Three-dimensional (3D) digital documentation represents a prominent form of mapping in the contemporary era, and its value is widely recognized. However, cost and portability constraints often limit its widespread use in routine research and conservation initiatives. This study proposes a cost-effective and portable approach to 3D digital documentation, employing everyday-carry (EDC) equipment, the iPhone 15 Pro and DJI Mini 4 Pro, for data acquisition in architectural heritage. The workflow was subsequently optimized, and the datasets from the iPhone-LiDAR and microdrone were seamlessly integrated, resulting in an integrated 3D digital model of both the indoor and outdoor spaces of the architectural heritage site. The model demonstrated an overall relative error of 4.93%, achieving centimeter-level accuracy, precise spatial alignment between indoor and outdoor sections, clear and smooth texture mapping, high visibility, and suitability for digital display applications. This optimized workflow leverages the strengths of both EDC equipment types while addressing the limitations identified in prior studies. Full article

Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R² = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R² ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data. Full article

Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. Full article

The growing demand for high-accuracy mapping and 3D modeling using unmanned aerial vehicles (UAVs) has accelerated advancements in flight dynamics, positioning accuracy, and imaging technology. Structure from motion (SfM), a computer vision-based approach, is increasingly replacing traditional photogrammetry through facilitating the automation of processes such as aerial triangulation (AT), terrain modeling, and orthomosaic generation. This study examines methods to enhance the accuracy of SfM-based AT through real-time kinematic (RTK) UAV imagery, focusing on large-scale infrastructure applications, including a dam and its entire basin. The target area, primarily consisting of homogeneous water surfaces, poses considerable challenges for feature point extraction and image matching, which are crucial for effective SfM. To overcome these challenges and improve the AT accuracy, a constraint equation was applied, incorporating weighted 3D coordinates derived from RTK UAV data. Furthermore, oblique images were combined with nadir images to stabilize AT, and confidence-based filtering was applied to point clouds to enhance geometric quality. The results indicate that assigning appropriate weights to 3D coordinates and incorporating oblique imagery significantly improve the AT accuracy. This approach presents promising advancements for RTK UAV-based AT in SfM-challenging, large-scale environments, thus supporting more efficient and precise mapping applications. Full article

The work developed aims to present an innovative methodology to execute the heritage conservation processes in a collaborative and interdisciplinary Building Information Modeling (BIM) project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with an architectural survey using terrestrial laser scanning (TLS) and terrestrial photogrammetry software, Structure from Motion (SfM), studying study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the Cyclone 3DR post-processing software, which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it was possible to develop an efficient Scan-to-HBIM workflow, where the Heritage BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, and quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural heritage, focusing on the deformations and deterioration of the masonry in columns and pilasters. To make multidisciplinary conservation work more flexible, specific properties have been created for the identification and analysis of the degradation detected in the structures, with the HBIM project constituting a manager of the control and inspection activities. The restoration technician interacts with the determined 3D element to mark the “type decay”, managing the properties in the element’s own definition window. Interactive schemes have been defined that incorporate the items for the mapping of the elements, as well as particular properties of a conservation process (intervention, control, and maintenance). All listed parametric elements have links to be viewed in 2D and 3D views. Therefore, the procedure has facilitated the auscultation of the scanned element as it is semantically delimited, the parametric modeling of it, the analytical study of its materials and deterioration, and the association of intrinsic parameters so that they can be evaluated by all the intervening agents. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment. Full article

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments. Full article

Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R²) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants. Full article

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use. Full article

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20% in terms of point cloud density and number of model facets, while the hardware cost was only 1% of that associated with LiDAR. Full article

UAV-SfM techniques are in constant development to address the challenges of accurate and precise mapping in terrains with complex morphologies. In contrast with the traditional photogrammetric processes, where only nadir images were considered, the combination of those with oblique imagery, also called off-nadir, has emerged as an optimal solution to achieve higher accuracy in these kinds of landscapes. UAV flights at a constant height above ground level (AGL) have also been considered a possible alternative to improve the resulting 3D point clouds compared to those obtained from constant height above mean sea level (AMSL) flights. The aim of this study is to evaluate the effect of incorporating oblique images as well as the type of flight on the accuracy and precision of the point clouds generated through UAV-SfM workflows for terrains with complex geometries. For that purpose, 58 scenarios with different camera angles and flight patterns for the oblique images were considered, 29 for each type of flight (AMSL and AGL). The 3D point cloud derived from each of the 58 scenarios was compared with a reference 3D point cloud acquired with a terrestrial laser scanner (TLS). The results obtained confirmed that both incorporating oblique images and using AGL flight mode have a positive effect on the mapping. Combination of nadir image blocks, obtained from an AGL crosshatch flight plan, with supplemental oblique images collected with a camera angle of between 20° and 35° yielded the best accuracy and precision records. Full article

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. Full article