Novel Pole Photogrammetric System for Low-Cost Documentation of Archaeological Sites: The Case Study of “Cueva Pintada”

19 p.Close-range photogrammetry is a powerful and widely used technique for 3D reconstruction of archaeological environments, specifically when a high-level detail is required. This paper presents an innovative low-cost system that allows high quality and detailed reconstructions of indoor complex scenarios with unfavorable lighting conditions by means of close-range nadir and oblique images as an alternative to drone acquisitions for those places where the use of drones is limited or discouraged: (i) indoor scenarios in which both loss of GNSS signal and need of long exposure times occur, (ii) scenarios with risk of raising dust in suspension due to the proximity to the ground and (iii) complex scenarios with variability in the presence of nooks and vertical elements of different heights. The low-altitude aerial view reached with this system allows high-quality 3D documentation of complex scenarios helped by its ergonomic design, self-stability, lightness, and flexibility of handling. In addition, its interchangeable and remote-control support allows to board different sensors and perform both acquisitions that follow the ideal photogrammetric epipolar geometry but also acquisitions with geometry variations that favor a more complete and reliable reconstruction by avoiding occlusions. This versatile pole photogrammetry system has been successfully used to 3D reconstruct and document the “Cueva Pintada” archaeological site located in Gran Canaria (Spain), of approximately 5400 m2 with a Canon EOS 5D MARK II SLR digital camera. As final products: (i) a great quality photorealistic 3D model of 1.47 mm resolution and ±8.4 mm accuracy, (ii) detailed orthophotos of the main assets of the archaeological remains and (iii) a visor 3D with associated information on the structures, materials and plans of the site were obtained.S

Digitalni prikaz oštećenih armiranobetonskih mostova

Inspekcija je već decenijama standardni postupak u ocenjivanju stanja mosta...Inspection of bridges has been a standard assessment procedure for decades..

What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}

Digital representation of as-damaged reinforced concrete bridges

Inspection of bridges has been a standard assessment procedure for decades. Its purpose is to identify and record all defects of the bridge structure. Normally used inspection techniques are rather simple, mainly relying on visual assessment. This dissertation proposes an improvement of concrete bridge inspection in terms of visual data acquisition, damage identification and digital representation of the bridge with identified damages. Instead of depending strictly on the human eye, photogrammetrically obtained 3D point clouds are used to identify and extract concrete damage features. As the most comprehensive substitute for the old-fashioned inspection report, Bridge Information Model (BrIM) is used as an inventory and inspection data repository. An Industry Foundation Classes (IFC) semantic enrichment framework is proposed to inject the extracted and reconstructed damage features into the as-is IFC model. After the general data model for damage description and its IFC representation are established, the method for generating the as-is IFC model of the bridge is proposed. Damage is identified as a deviation of the as-is geometry, represented by the 3D point cloud, from the as-built geometry, represented by BrIM. Geometric and semantic enrichment of the IFC model is achieved by injecting the reconstructed 3D meshes representing damaged regions and corresponding BMS catalogbased damage information. The proposed method uses Constructive Solid Geometry (CSG) Boolean operations to geometrically enrich the IFC geometry elements, which align with corresponding damage regions from the as-is point cloud. Damage information (e.g., type, extent, and severity) is structured so that it complies to the BMS data structure. Finally, the proposed data model, damage identification, feature extraction, and semantic enrichment method are validated in the presented case study

Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy