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Although the field of geomatics has seen multiple technological advances in recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; on the other hand, portable laser scanning is time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulting working prototype has been put to the test to verify the effectiveness of the proposed approach.

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

The popularity of automatic photogrammetric techniques has promoted many experiments in underwater scenarios leading to quite impressive visual results, even by non-experts. Despite these achievements, a deep understanding of camera and lens behaviors as well as optical phenomena involved in underwater operations is fundamental to better plan field campaigns and anticipate the achievable results. The paper presents a geometric investigation of a consumer grade underwater camera housing, manufactured by NiMAR and equipped with a 7′′ dome port. After a review of flat and dome ports, the work analyzes, using simulations and real experiments, the main optical phenomena involved when operating a camera underwater. Specific aspects which deal with photogrammetric acquisitions are considered with some tests in laboratory and in a swimming pool. Results and considerations are shown and commented.

This paper examines the survey of tall buildings in an emergency context like in the case of post-seismic events. The after-earthquake survey has to guarantee time-savings, high precision and security during the operational stages. The main goal is to optimize the application of methodologies based on acquisition and automatic elaborations of photogrammetric data even with the use of Unmanned Aerial Vehicle (UAV) systems in order to provide fast and low cost operations. The suggested methods integrate new technologies with commonly used technologies like TLS and topographic acquisition. The value of the photogrammetric application is demonstrated by a test case, based on the comparison of acquisition, calibration and 3D modeling results in case of use of a laser scanner, metric camera and amateur reflex camera. The test would help us to demonstrate the efficiency of image based methods in the acquisition of complex architecture. The case study is Santa Barbara Bell tower in Mantua. The applied survey solution allows a complete 3D database of the complex architectural structure to be obtained for the extraction of all the information needed for significant intervention. This demonstrates the applicability of the photogrammetry using UAV for the survey of vertical structures, complex buildings and difficult accessible architectural parts, providing high precision results.

The capture of 3D reality has demonstrated increased efficiency and consistently accurate outcomes in architectural digitisation. Nevertheless, despite advancements in data collection, 3D reality-based modelling still lacks full automation, especially in the post-processing and modelling phase. Artificial intelligence (AI) has been a significant focus, especially in computer vision, and tasks such as image classification and object recognition might be beneficial for the digitisation process and its subsequent utilisation. This study aims to examine the potential outcomes of integrating AI technology into the field of 3D reality-based modelling, with a particular focus on its use in architecture and cultural-heritage scenarios. The main methods used for data collection are laser scanning (static or mobile) and photogrammetry. As a result, image data, including RGB-D data (files containing both RGB colours and depth information) and point clouds, have become the most common raw datasets available for object mapping. This study comprehensively analyses the current use of 2D and 3D deep learning techniques in documentation tasks, particularly downstream applications. It also highlights the ongoing research efforts in developing real-time applications with the ultimate objective of achieving generalisation and improved accuracy.

The increasing request for digitized data among several fields, including the built environment and cultural heritage (CH), highlights the need for proficient ways to access, archive, and share 3D data and related information among users. The sector of reality capture produces accurate and reliable products that can support building management and CH maintenance, at the price of heavy and resource-demanding data. An emerging solution to this problem is represented by the web platforms for 3D data management, that promise to relieve users from the costs of archive and hardware, providing effective visualization, access and sharing tools. The panorama of commercial web platforms is analyzed according to the Software-as-a-Service business model, and the features of some representative platforms are exposed. The paper discusses the main advantages of diffused access and collaboration and the potential issues concerning long-term archival and data persistence. It provides a general overview of the main available platforms and describes their main features, comparing their specific pros and cons according to their category. The future perspectives of the web platform sector are promising as, according to the current development path, they may be able to empower built environments and the CH sector with a diffused, systematic, and conscious use of 3D data.

Multi-camera Visual Simultaneous Localization and Mapping (V-SLAM) increases spatial coverage through multi-view image streams, improving localization accuracy and reducing data acquisition time. Despite its speed and generally robustness, V-SLAM often struggles to achieve precise camera poses necessary for accurate 3D reconstruction, especially in complex environments. This study introduces two novel multi-camera optimization methods to enhance pose accuracy, reduce drift, and ensure loop closures. These methods refine multi-camera V-SLAM outputs within existing frameworks and are evaluated in two configurations: (1) multiple independent stereo V-SLAM instances operating on separate camera pairs; and (2) multi-view odometry processing all camera streams simultaneously. The proposed optimizations include (1) a multi-view feature-based optimization that integrates V-SLAM poses with rigid inter-camera constraints and bundle adjustment; and (2) a multi-camera pose graph optimization that fuses multiple trajectories using relative pose constraints and robust noise models. Validation is conducted through two complex 3D surveys using the ATOM-ANT3D multi-camera fisheye mobile mapping system. Results demonstrate survey-grade accuracy comparable to traditional photogrammetry, with reduced computational time, advancing toward near real-time 3D mapping of challenging environments.

The paper presents the first part of a research project concerning the creation of 3D terrain models useful to understand landslide movements. Thus, it illustrates the creation process of a multi-source high-resolution Digital Terrain Model (DTM) in very dense vegetated areas obtained by integrating 3D data coming from three sources, starting from long and medium-range Terrestrial Laser Scanner up to a Backpack Indoor Mobile Mapping System. The point clouds are georeferenced by means of RKT GNSS points and automatically filtered using a Cloth Simulation Filter algorithm to separate points belonging to the ground. Those points are interpolated to produce the DTMs which are then mosaicked to obtain a unique multi-resolution DTM that plays a crucial role in the detection and identification of specific geological features otherwise visible. Standard deviation of residuals of the DTM varies from 0.105 m to 0.176 m for Z coordinate, from 0.065 m to 0.300 m for X and from 0.034 m to 0.175 m for Y. The area under investigation belongs to the Municipality of Piuro (SO) and includes both the town and surrounding valley. It was affected by a dramatic landslide in 1618 that destroyed the entire village. Numerous challenges have been faced, caused both by the characteristics of the area and the processed data. The complexity of the case study turns out to be an excellent test bench for the employed technologies, providing the opportunity to precisely identify the needed direction to obtain future promising results.

This paper presents a comprehensive scoping review of the application of 3D digital technologies in the documentation, conservation, and management of historic gardens and related cultural heritage. By analyzing a curated selection of literature, this study assessed the current state of research, highlighting trends in publications, the geographic distribution of contributors, and the key technologies employed. Using bibliometric methods and visualization tools, followed by a case study review, this review identified significant research hotspots and technical methodologies, particularly focusing on advanced techniques such as mobile laser scanning, UAV photogrammetry, and point cloud processing and their relationships with end users. The findings emphasize the importance of integrating multiple technologies to capture the diverse elements of historic gardens, including architectural features, vegetation, and topography. This review also underscores the significance of dynamic landscapes facing challenges posed by environmental degradation and urban development pressures. Moreover, it discusses the limitations of existing research and outlines future opportunities, such as the development of 4D documentation systems and the incorporation of AI for improving heritage management. This paper concludes by recommending interdisciplinary collaboration and public engagement to enhance the accessibility, understanding, and sustainable management of historic gardens through innovative technological applications.

In recent years, the investigation and 3D documentation of architectural heritage has made an efficient digitalization process possible and allowed for artificial intelligence post-processing on point clouds. This article investigates the multilevel multiresolution methodology using machine learning classification algorithms on three point-cloud projects in China: Nanchan Ssu, Fokuang Ssu, and Kaiyuan Ssu. The performances obtained by extending the prediction to datasets other than those used to train the machine learning algorithm are compared against those obtained with a standard approach. Furthermore, the classification results obtained with an MLMR approach are compared against a standard single-pass classification. This work proves the reliability of the MLMR classification of heritage point clouds and its good generalizability across scenarios with similar geometrical characteristics. The pros and cons of the different approaches are highlighted.