Terrestrial Laser Scanning Research Articles

18-years of high-Alpine rock wall monitoring using terrestrial laser scanning at the Tour Ronde east face, Mont-Blanc massif

Since the end of the 20th century, each decade has been warmer than the previous one in the European Alps. As a consequence, Alpine rock walls are generally facing high rockfall activity, likely due to permafrost degradation. We use a unique terrestrial laser scanning derived rockfall catalog over 18 years (2005–2022) compared with photographs (1859–2022) to quantify the evolution of the east face of Tour Ronde (3440–3792 m a.s.l.) in the Mont-Blanc massif (western European Alps) that is permafrost-affected. Overall, 210 rockfalls were identified, from 1 to 15 500 m3. Forty-five events were >100 m3 while cumulated volume of events <10 m3 represents <1% of the fallen rocks. The rockfall magnitude-frequency distribution of the overall inventory follows a power law, with a mean exponent b of 0.44 ± 0.03, characterizing a high contribution of large rockfalls. The depth of failure ranges from a few centimeters to more than 20 m while 95% of the rockfalls depth is <5 m, highlighting the role of the active layer. The mean rock wall erosion rate is 18.3 ± 0.2 mm yr−1 for the 2005–2022 period and ranks in the top range of reported values in the Alps. It has greatly increased between the periods 2006–2014 and 2016–2022, probably in relation to a series of summer heat waves. The exceptional erosion rate of 2015 is driven by one large rockfall in August. Since 2006, an ice apron that covered 16 100 m2 has now almost vanished, and the surface of the glacier du Géant at the rock wall foot has lowered by several tens of meters. The retreat of these two ice masses contributed to the rock wall instability as more than 35% of the rockfall volume detached from the deglaciated surfaces.

Evaluation of mobile laser scanning acquisition scenarios for automated wood volume estimation in a temperate hardwood forest using quantitative structural models

This study explores how data from a handheld mobile laser scanning (MLS) system and quantitative structural models (QSM) can be used to estimate tree structural attributes. Four MLS acquisition scenarios were investigated in a 1 ha temperate hardwood stand, including 15 and 35 m parallel lines, nine circular plots, and a 20 m × 20 m grid. Results were compared against terrestrial laser scanning and destructive field measurements. All acquisition scenarios yielded comparable results, except for the 35 m scenario, which showed greater variability. The 20 m × 20 m grid scenario showed the highest accuracy, with an RMSE of 0.41 m (2.07%) for tree height, 3.98 cm (14.93%) for diameter at breast height, 0.21 m³ (19.28%) for merchantable wood volume, and 0.07 m³ (10.11%) for merchantable stem volume. A bias &lt; 5% was observed for these key attributes, except for an 11.68% bias in merchantable wood volume. Overestimation of branch volume was identified as the primary source of bias related to merchantable wood volume. This study highlights MLS’s potential for accurate, non-destructive estimation of tree structural attributes, while pointing out the need to refine noise removal and to assess the most suitable acquisition scenarios for various forest types.

Comparative Analysis of Non-Invasive Measurement Methods for Optimizing Architectural Documentation

Architectural documentation not only plays a critical role in the conservation of historical structures, but also enables their detailed comprehension of the structure. This study aims to assess the most effective methods for drawing and modeling architectural structures and present their advantages and disadvantages. Measurements play a significant role in this context, and today's technology offers the potential to accelerate this process and enhance accuracy. However, the application of these technologies can impose additional burdens such as elevated expenses, the requisite for specialized personnel, and the management of substantial data volumes. Therefore, determining the appropriate measurement method in line with the quality of architectural documentation is essential. For this study, the Mosque of Kurşunlu Complex in Eskişehir was selected for its historical and topographical attributes which enabled all methods to be examined. The data produced via terrestrial laser scanning, aerial photogrammetry and terrestrial photogrammetry methods were examined in terms of the production of drawings and models for different analysis methods such as structure, daylight and building acoustics, as well as survey drawings required for the architectural documentation processes of the building. The study concluded that no single method could produce holistic data on its own, and the best results for comprehensive documentation were achieved by integrating terrestrial laser scanning and aerial photogrammetry. Furthermore, for products that do not require comprehensive data, photogrammetric methods were more efficient.

Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams.

Correction: Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

Correction: Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations

Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.

Analysis on Using 3D Scanning and BIM to Reduce the Physical and Non-Physical Construction Waste for Sustainable Fireproofing of Steel Trusses

Embracing the archaic methods used in supervision and/or management in the field of architecture, engineering, and construction (AEC) creates the imperative need to adopt and/or develop sustainable methods to reduce construction physical waste, as well as the non-physical waste. Thus, the articles present a method that convenes three different specializations, namely geodesy, civil engineering, and architecture, that make use of the 3D terrestrial laser scanner (TLS), 3D reconstruction, and the 3D analysis to make an accurate bill of quantity (BOQ) to be able to assess the quality of the construction in compliance with the idea of sustainable development in the AEC industry. This article describes the three major parts: obtaining the point cloud, the 3D modeling, and extracting the relevant data. The novelty of the research is threefold: (i) a viable methodology for obtaining an accurate BOQ by reducing the non-physical waste to avoid cost and time overruns; (ii) the significance and impact of a (a) strictly automated and (b) automatic with traditional survey to obtain the 3D model of the point cloud on the resulting area that requires rehabilitation or to be covered with intumescent paint; and (iii) a detailed analysis of the specific elements that can blunder the final 3D model, such as the shadowing effect that can appear in very complex construction structures. Moreover, the proposed methodology represents a significant advancement in optimizing 3D modeling to improve the comprehension of steel trusses in the field of AEC sector.

Intricacies of Opening Geometry Detection in Terrestrial Laser Scanning: An Analysis Using Point Cloud Data from BLK360

This study investigates the use of terrestrial laser scanning (TLS) in urban excavation sites, focusing on enhancing ground deformation detection by precisely identifying opening geometries, such as gaps between pavement blocks. The accuracy of TLS data, affected by equipment specifications, environmental conditions, and scanning geometry, is closely examined, especially with regard to the detection of openings between blocks. The experimental setup, employing the BLK360 scanner, aimed to mimic real-world paving situations with varied opening widths, allowing an in-depth analysis of how factors related to scan geometry, such as incidence angles and opening orientations, influence detection capabilities. Our examination of various factors and detection levels reveals the importance of the opening width and orientation in identifying block openings. We discovered the crucial role of the opening width, where larger openings facilitate detection in 2D cross-sections. The overall density of the point cloud was more significant than localized variations. Among geometric factors, the orientation of the local object geometry was more impactful than the incidence angle. Increasing the number of laser beam points within an opening did not necessarily improve detection, but beams crossing the secondary edge were vital. Our findings highlight that larger openings and greater overall point cloud densities markedly improve detection levels, whereas the orientation of local geometry is more critical than the incidence angle. The study also discusses the limitations of using a single BLK360 scanner and the subtle effects of scanning geometry on data accuracy, providing a thorough understanding of the factors that influence TLS data accuracy and reliability in monitoring urban excavations.

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

Digital preservation of classical gardens at the San Su Shrine

Chinese classical gardens, an essential part of World Cultural Heritage, are well exemplified by Xishu Garden as a notable branch of this tradition. This research focuses on the San Su Shrine—a quintessential example of Xishu Garden—and explores the application of digital technologies in the commemorative research and quantitative preservation of these gardens. By integrating terrestrial laser scanning and terrestrial digital photogrammetry, we acquired quantitative data and constructed digital models to innovate upon the methodological approach to garden conservation. The innovation of this study is its analysis of Xishu Garden’s commemorative spaces through both temporal and spatial dimensions. 1) Temporally, this study examines the evolution and stratification of the garden's commemorative elements across various historical periods. 2) Spatially, it explores the interconnectedness of commemorative elements within the garden's buildings, rockery stones, water landscape and plant landscape. This dual-faceted approach yields novel insights and methods for assessing the universal value of the commemorative aspects of Xishu Garden. Moreover, it promotes the application of digital technology in the study and preservation of Xishu and other Chinese classical gardens.

Supervised terrestrial to airborne laser scanner model calibration for 3D individual-tree attribute mapping using deep neural networks

Capturing subcanopy forest information from airborne laser scanning (ALS) is constrained by signal occlusion. This study demonstrates the potential of close-range terrestrial laser scanner (TLS) scanning to mitigate the constraints of ALS in acquiring stem-level forest attributes. A transformer-based neural network was adapted to classify and segment 3D individual trees from ALS data. A deep neural network combined with a gaussian process layer was proposed to estimate tree diameter-at-breast-height (DBH) from ALS data. The performance of these methods was compared to other benchmarked methods using the same dataset, including a total of seven classifiers, five segmentors, and six attribute regressors. The study was conducted across four ALS sample areas and ten combined TLS/ALS plots, primarily in montane forests. Manual delineation of TLS trees provided a precise validation reference. The proposed methods demonstrated high accuracies, with a mean intersection-over-union (mIoU) of 0.92 for ALS tree classification, 0.70 for tree segmentation, and a RMSE of 4.2 cm or 18.9 % for DBH estimation on average of the ten plots. Tree detection accuracy was strongly associated with the final segmentation accuracy. Factors such as tree height, overlapping, inclination, and neighboring conditions impacted segmentation accuracy. Our segmentation method effectively mitigated accuracy loss for short and occluded trees. Overall, this study presents scalable and cost-effective solutions for TLS calibration of ALS scans over two meso-scale montane valleys. Leveraging deep neural networks enables scaling of stem attributes to landscape scales, thereby linking fine-scale forest inventory with sustainable management of expansive forest resources. Our codes are available at https://github.com/truebelief/artemis_treescaling.

Digital Technology: A Step to Protect Cave Art of Indian Subcontinent as National Heritage – A Review

Abstract The integration of digital technology, particularly virtual reality, is proving to be a pivotal tool in preserving and sharing the rich cultural heritage found within Cave art sites. This article deliberates on the use and application of digital technology in the field of cave painting conservation that provides an authentic and effective method of Cave art preservation for the future. Employing advanced techniques of photogrammetry, terrestrial laser scanning, structured light scanning, and high-resolution photography can digitally preserve the intricate details of Cave art, while the utilization of humidity, temperature, and CO2 sensors may provide a comprehensive digital monitoring system to track the condition of Cave art over time. This paper deals with the evolving landscape of technologies and their application to safeguard Cave art from environmental degradation and anthropogenic factors. The digitization of cave art has the potential to accurately conserve a site if used in an effective manner while also allowing visitors to experience this art form in ways never before feasible. The application of advanced digital technology in cave art conservation is not merely a choice but a necessity, considering the impermanent nature of these paintings in their natural environment. This paper underscores the urgency and significance of leveraging digital tools to ensure the enduring legacy of Cave art, an example of how digital technologies, as they evolve, will play an increasingly essential role in the conservation and dissemination of our world’s extraordinary cultural heritage.

Moths in space: The below-canopy structure of forest drives abundance and mobility-related traits of moth communities

The distribution of species is primarily driven by the availability of trophic resources. In a given forest type, insects trophically related to the dominant tree are expected to be evenly distributed due to the abundance of their foodplant. However, their distribution is also influenced by complex relationships with abiotic and biotic parameters such as available space, predatory pressure, and morphometric traits. In this study, we investigated how the three-dimensional structure of space below the canopy may affect the composition of nocturnal lepidoptera communities. To synthesise the complexity of the dispersal behaviour of these insects, we evaluated easily measurable traits such as wingspan and the presence of tympanic organs, both connected to their mobility and thus potentially influenced by the structure of the available flight space. The study was conducted in the Sila National Park (Italy), where 12 sampling sites were selected in pine forests and an additional 12 in beech forests. Forest spatial structure was investigated using a portable terrestrial laser scanner. Moths were sampled monthly using light traps from May to October in both 2019 and 2020. Among measured forest traits, we observed that the space above three meters from the ground is the only factor influencing community composition. Larger species with tympanic organs prefer environments with less space below tree canopies. Our findings could be the starting point for future studies that investigate a potential defence strategy of moths against bats, as tympanate and larger species not only actively avoid chiropter predation but could also choose denser forests because of a lower bat activity. Moths' distribution and community composition thus appear to be significantly shaped by the spatial structure of forests.

Enhancing the worship experience: a quantitative approach to the Şehzade and Süleymaniye discussion

Süleyman I and Mimar Sinan stand as influential figures of the Ottoman Empire, leaving an indelible mark on Istanbul's cultural and architectural landscape. Built by Sinan for Süleyman the Magnificent, Şehzade Mehmed and Süleymaniye Mosques in particular have been subjects of debate. Şehzade is described by many scholars as a peak in sixteenth century Ottoman architecture, whilst Süleymaniye, built shortly after, is viewed as an architectural regression. This article investigates the transition from the symmetrical quatrefoil scheme of Şehzade to Süleymaniye’s basilica-like design with quantitative methods, based on certain design principles. Spatial perception, visibility, and daylight analyses were conducted on models and plan drawings based on 3D point cloud data obtained from terrestrial laser scanning. Spatial perception analyses suggest that Sinan intended to influence users’ perception with the formation of the superstructure. Resulting visibility levels of certain focal points in the sanctum area prove how they were pivotal in the design process. Finally, daylight analyses results indicate how daylight was also a significant design criterion for Sinan. Ultimately, the study demonstrates that Süleymaniye’s floor plan is an improved design addressing the spatial issues that exist in Şehzade, highlighting Mimar Sinan’s effort to enhance the worship experience.

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Cable-stayed bridge all-round morphology identification based on 3D point cloud model

Traditional inspection methods, that is, total station, can be labor-intensive, time-consuming, and efficiently challenging, especially for long-span cable-supported bridges. Recently, Terrestrial Laser Scanning (TLS) has been attractive in bridge digitization because of its non-contact and high-efficiency features. It can provide a comprehensive and all-round morphology assessment through establishing the bridge’s 3D point cloud model. Based on our previous research on point cloud registration of long-span bridges, this article focuses on spatial form identification using the registered point cloud model, specifically the identification of cable sag and girder spatial volume. In detail, precisely monitoring cable sag is critical for cable force identification, while accurate girder volume is essential bridge information, which should be tracked regularly and used for long-term performance evaluation and maintenance guidance. To evaluate the precision of the proposed TLS-based inspection solution, a case study is conducted on a 152 + 370 + 152 m cable-stayed bridge. Results show the identifying error of TLS-based solution remains at 2% for cable sag identification and 5% for girder volume recognition. The traffic-induced sag variance can also be captured by the point cloud data. Therefore, the TLS-based inspection method is thoroughly evaluated and demonstrated as a highly efficient and trustworthy solution.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

ABSTRACT Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments.

EVALUATION OF NERF 3D RECONSTRUCTION FOR ROCK ART DOCUMENTATION

Abstract. Digital documentation of rock art traditionally relies on a point cloud captured by a terrestrial laser scanner (TLS) or derived from an oriented image obtained using photogrammetry. In modern photogrammetry, the dense point cloud is generated using multi-view stereo (MVS) and subsequently used to generate a photorealistic 3D model. A recent method to reconstruct 3D models from images is Neural Radiance Fields (NeRF), which uses volume density to render the scenes through neural networks. The advantage of NeRF is that it can construct 3D models faster without using high computer processors and memory. NeRF has been studied in various applications, including cultural heritage, but not specifically for rock art documentation. Therefore, this paper evaluates three-dimensional (3D) reconstruction techniques using NeRF on Nerfstudio platform on two rock art datasets and compares them with the point cloud and 3D mesh models obtained from TLS and photogrammetry/MVS. The results have shown that NeRF does not match MVS in achieving geometric precision and texture quality. However, its learning-based approach accelerates reconstruction and offers potential enhancements to complement photogrammetric workflow.

FROM 3D SURVEY TO DIGITAL REALITY OF A COMPLEX ARCHITECTURE: A DIGITAL WORKFLOW FOR CULTURAL HERITAGE PROMOTION

Abstract. In recent years, the digitalization and dissemination of historical heritage have become crucial nodes in the preservation and valorization of Cultural Heritage (CH). Technologies such as Unmanned Aerial Vehicle (UAV) and terrestrial photogrammetry, Terrestrial Laser Scanning (TLS) and handheld Simultaneous Localisation and Mapping (SLAM) laser scanning allow the generation of digital models of architecture that can be explored through interactive web platforms, such as those based on WebGL graphic library. These are considered one of the most promising innovations for digitizing and sharing CH site due to their application in a wide range of contexts, promoting new forms of interaction with architecture at different scales. Additionally, the use of geomatic tools allows for a more complete 3D reconstruction and evaluation of the results by comparing different techniques. The article focuses on digitization as a tool for documenting and sharing CH assets, with the aim of developing a replicable prototype platform for an immersive Virtual Tour (VT) of an art collection and the architectural complex in which it is resided. In addition, this paper presents the results of a case study conducted at the Ricci Oddi Gallery of Modern Art in Piacenza, Italy. The source code of the implemented application is available on GitHub to permit replicability for other case studies.