Hand-held Laser Research Articles

Characteristics and Surgical Outcomes of Laser-induced Full-thickness Macular Holes

Purpose: To report the characteristics and the surgical outcomes of full-thickness macular holes (MHs) accidentally caused by laser devices and analyze the visual predictors. Methods: 33 eyes that underwent vitrectomy for laser-induced MHs were included. Full ophthalmic examinations and optical coherence tomography were used for assessment at baseline and at follow-ups. Preoperative and 6-months postoperative features were analyzed respectively as potential predictors of final visual acuity. Results: 11 eyes were injured by a neodymiumdoped yttrium aluminum garnet (Nd:YAG) laser and 22 by a handheld laser. 5/33 eyes had a focal subfoveal choroidal depression preoperatively. MH closure was achieved in 87.9% eyes. 79.3% eyes had VA improvement of 2 or more lines. Compromised VA improvement was associated with preoperative choroidal depression (P=0.026, odds ratio [OR]=0.068, 95% confidence interval [CI]: 0.006∼0.729). Worse final BCVA was associated with worse preoperative BCVA (P=0.001, regression coefficient [B]=0.497, 95%CI: 0.221∼0.774), preoperative choroidal depression (P=0.014, B=0.353, 95%CI: 0.078∼0.628), Nd:YAG laser injury (P=0.030, B=-0.244, 95%CI: -0.461∼-0.026), larger MH diameter (P=0.023, B=0.031, 95%CI: 0.005∼0.058), failed macular hole closure (P=0.003, B=-0.472, 95%CI: -0.775∼-0.169), larger ellipsoid zone (EZ) defect at 6 months (P=0.002, B=0.053, 95%CI: 0.024∼0.082) and thinner foveal neuroretina thickness at 6 months (P<0.001, B=-0.365, 95%CI: -0.520∼-0.211). Conclusion: Vitrectomy is effective in closing laser-induced MHs and improving visual acuity. Preoperative factors including worse preoperative BCVA, choroidal depression, Nd:YAG laser injury and larger MH diameter and postoperative factors including failed MH closure, larger EZ defect and thinner foveal neuroretina at 6 months are predictive factors for worse final VA.

Analysis and comparison of methods for determining small piles of wood chips using laser scanning technology

The monitoring of forest biomass stock volumes in larger operations is typically conducted irregularly, either by tracking cargo arrivals or by using simple manual measurement methods. The objective of this study was to assess the accuracy of smart methods based on laser scanning technology, integrated into mobile phones and a handheld laser scanner, for measuring smaller piles of forest chips. For the experiment, a total of 50 m³ of fiberwood logs were chipped and distributed into four piles. The smart solutions selected for laser scanning of forest biomass in this study were the Stonex Geoslam X120 GO handheld laser scanner and the iPhone 14 Pro Max equipped with a LiDAR sensor. The results were influenced by the selected conversion coefficient and the exclusion of small scattered fragments of forest chips around the piles, which were not included in the final volume calculation. The smallest discrepancy identified by the smart solutions was 3 m³ (6%) of woody mass. The findings demonstrated that the smart solutions utilizing LiDAR technology offer good affordability, ease of use, and satisfactory accuracy. They are user-friendly and provide quick results.

Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data

ABSTRACT Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.

Joint neural denoising and consolidation for portable handheld laser scan

Abstract. Mobile and handheld laser scanners document scenes in an economical manner, but the data they acquire are often noisy, of low resolution, unevenly distributed, and feature voids within the scanned scene. These characteristics challenge such applications as feature extraction and 3D modeling when processing the raw pointset. To date, point cloud denoising and consolidation (address of distribution and void regions) have been treated independently despite their complementary nature and their mutual dependence on the underlying surface representation. We argue that if treated jointly, richer shape context features can be learned and an improved enhancement framework can be derived. Accordingly, we formulate the shape context description as a joint contribution by both denoising and consolidation, within an end-to-end framework. To this end, we introduce densely packed graph convolution layers to extract contextual information, allowing to query points offset to the underlying surface and to compensate for the structural loss. We demonstrate how the commonly used L2-driven loss functions generate non-smooth output and volume shrinkage, and alleviate this by ones that mitigate the noisy outcome, repair voids, and improve point density distributions. Performance analysis on benchmark datasets demonstrates how we outperform state-of-the-art solutions, produce high-fidelity outcomes, and improve reconstruction-based tasks in real-world setups.

Preliminary Investigation of Dust Generation in Granite Quarry

Abstract Dust dispersion from granite surface quarries substantially influences the health of workers and nearby neighbourhoods. A preliminary study was conducted at a granite quarry to investigate the factors that contribute to the emission of dust in granite surface quarries and to develop effective dust control measures. The particulate matter from areas within the site was measured using a Handheld Laser Particle Counter Model 3887. The preliminary results revealed the presence of PM0.3, PM0.5, and PM5.0 at their least value of 4.85×107µm/m3, 1.18×107µm/m3, and 2.83×104µm/m3 respectively. While, the highest value ranges of 6.22×107µm/m3, 7.68×106µm/m3, and 9.89×103µm/m3 respectively, and is considered to be beyond hazardous. The preliminary readings indicate that the air quality is extremely poor and that there is a high risk of health problems, even for healthy people. The initial results of this study suggest that dust generation in granite surface quarries is a complex process influenced by various factors, including the type of rock being quarried, the blasting methods used, and the prevailing meteorological conditions. To better understand the factors that contribute to dust generation and to devise effective dust control measures, additional research is required.

Multi-Platform Point Cloud Registration Method Based on the Coarse-To-Fine Strategy for an Underground Mine

Spatially referenced and geometrically accurate laser scanning is essential for the safety monitoring of an underground mine. However, the spatial inconsistency of point clouds collected by heterogeneous platforms presents challenges in achieving seamless fusion. In our study, the terrestrial and handheld laser scanning (TLS and HLS) point cloud registration method based on the coarse-to-fine strategy is proposed. Firstly, the point features (e.g., target spheres) are extracted from TLS and HLS point clouds to provide the coarse transform parameters. Then, the fine registration algorithm based on identical area extraction and improved 3D normal distribution transform (3D-NDT) is adopted, which achieves the datum unification of the TLS and HLS point cloud. Finally, the roughness is calculated to downsample the fusion point cloud. The proposed method has been successfully tested on two cases (simulated and real coal mine point cloud). Experimental results showed that the registration accuracy of the TLS and HLS point cloud is 4.3 cm for the simulated mine, which demonstrates the method can capture accurate and complete spatial information about underground mines.

NEW TECHNOLOGIES AVAILABLE FOR MICRONEUROSURGICAL REMOVAL OF ACOUSTIC NEUROMAS

Introduction The aim of this study was to analyze the results of microsurgery removal of acoustic neuromas (AN) using new technologies: flexible hand-held laser fiber, Sonopet Ultrasound Aspirator, Facial Nerve “detector” monopolar stimulation (for localize position and course of facial nerve), BAER’s with level specific (LS) CE-Chirp stimulus for hearing preservation, endoscope for IAC final check, and injectable bone substitute for closure. We report a retrospective nonrandomized clinical study on 300 cases consecutively operated on. Material and Methods From July, 2010 to December, 2023, 350 consecutive patients suffering from AN have been operated on with microneurosurgical technique by key-hole retrosigmoid approach. In majority of cases tumor resection was performed with the aid of a handheld flexible laser fiber. In the same period, Sonopet Ultrasound aspirator was used for tumor debulking and/or opening of the internal auditory meatus and canal. From May 2015, hearing preservation by means LS CE-Chirp BAER was attempted in patients with preoperative socially useful hearing (AAO-HNS class A and B). In addition, we check the removal of tumor inside the internal auditory canal (IAC) by using the flexible and/or rigid endoscope, for completing tumor removal near the fundus. Results Overall time from incision to skin suture changed in relation to size of tumor (from 165 to 575 minutes) and was not affected by the use of hand-held laser. Facial nerve function was clinically assessed with the House-Brackmann (HB) scale preoperatively, in the early postoperative period (after 1 week), and at 6-month follow-up. In 3 cases a preoperative facial nerve palsy was observed (HB III and HB IV, respectively). In the remaining cases, 6 months after surgery facial nerve preservation rate (HB I) was 92%, Hearing preservation rate (AAO-HNS A and B, pre- and postoperatively) was 52% in eligible cases. Total and “nearly total” removal of tumor was possible in about 80% of cases. Dura closure was performed with underlaying autologous pericranium. Injectable bone substitute in the gaps after bone flap repositioning minimized postoperative CSF leakage and improved the aesthetic result. Conclusions The use of new technologies (handheld flexible laser fibers, Sonopet Ultrasound Aspirator, fluoreceine, Nimbus facial nerve continuous monitoring, LS CE-Chirp BAER, endoscope for IAC final check) in AN microsurgery seems to be safe and efficacious and facilitates tumor resection, especially in ‘‘difficult’’ conditions (e.g., large size, highly vascularized, or hard tumors). The good functional outcome following conventional microsurgery seems to be further improved and the extent of tumor removal could be increased with the proper use of the new technologies available in the neurosurgical armamentarium.

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner.

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China

Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of “field data collection—data processing—data analysis and application”, which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields.

Contribution of Dental Dimensions in Sex Prediction and Identification

The purpose of this research is to assess the differences of tooth measurements between Bulgarians and Spaniards. The survey included 232 Bulgarians aged 20–40 years. Vestibulo-lingual and mesio-distal dental diameters were obtained by a hand-held laser scanner (FastSCAN Cobra, Polhemus Inc., Colchester VT) and analyzed with SPSS 28.0. We detected significant differences in mesio-distal measurements of upper central incisors, premolars, and molars and mandibular first molars, vestibulo-lingual measurements of upper central incisors, canines and first molars and lower first molars between Bulgarians and Spaniards. Our findings revealed that tooth measurements vary in the populations and thus they can be reliable predictors in forensic identification.

Sodium copper chlorophyllin-loaded chitosan nanoparticle-based photodynamic therapy for B16 melanoma cancer cells.

Melanoma is one of the most aggressive and fatal skin cancers owing to its ability to metastasize and develop resistance to chemotherapy. Photodynamic therapy (PDT) is a minimally noninvasive treatment modality comprising photosensitizers (PSs), light sources, and endogenous molecular oxygen that exert a localized cytotoxic effect on cancer cells. The current study explores the therapeutic potential of sodium copper chlorophyllin-loaded chitosan nanoparticles (CH-SCC NPs) along with handheld laser-based PDT on B16 cancer cells. A modified chlorophyll derivative identified as sodium copper chlorophyllin (SCC) is a dietary supplement that has anticancer properties. Herein, we have synthesized CH-SCC NPs using the ionic gelation method to enhance the PS's bioavailability and efficiency. Chitosan nanoparticles exhibited high biocompatibility in a normal cell line L929, zebrafish, and chick embryos, and were successfully employed to deliver the SCC to cancer cells. CH-SCC NPs showed an enhanced PDT effect that killed approximately 80%-85% of B16 cells. CH-SCC NPs in combination with a handheld portable laser source showed significant therapeutic potential against the B16 skin cancer cell line. The experimental findings further strengthen our device-repurposing strategy, which suggests that SCC nanoformulations along with handheld laser can be a suitable treatment for skin cancer even in remote areas where power source and treatment cost can be a limitation.

Retention of p-Chip microtransponders and posttagging survival of small-bodied stream fishes

Abstract Objective Obtaining demographic rates often requires complex open-population capture–mark–recapture (CMR) study designs. Conducting such studies for small-bodied fishes has been limited in part by excessive mortality after tagging procedures and poor tag retention. As new tag types emerge, information regarding fish survival and tag retention over varying time scales may benefit resource managers to effectively plan future CMR studies. The p-Chip microtransponder is a 500- × 500- × 100-μm tag that is inserted subcutaneously and is read with a handheld laser. Each tag contains a nine-digit unique identification number. P-Chip microtransponder tags have been used on a limited number of small-bodied fishes, with relatively high rates observed for fish survival and tag retention. Information on posttagging survival and retention of p-Chip microtransponder tags across a range of small-bodied fish species and tagging locations is needed to inform their effectiveness in future CMR studies. Methods We quantified survival and tag retention after p-Chip microtransponder implantation in Creek Chub Semotilus atromaculatus and Northern Pearl Dace Margariscus nachtriebi in a laboratory environment, and retention of p-Chip microtransponder tags in Northern Pearl Dace was also quantified over a year-long field study. We marked 56 Creek Chub and 9 Northern Pearl Dace in the laboratory with p-Chip microtransponder tags and compared them to an equal number of unmarked individuals over 90 days. We marked 1990 Northern Pearl Dace and conducted recapture events through four seasons (June, September, and October 2022 and June 2023) in four headwater streams of Nebraska. Result Survival for Creek Chub was 85% (standard error [SE] = 5.9) and did not differ from control fish survival (95%; SE = 3.2) in the 90-day laboratory experiment. Survival for Northern Pearl Dace was 89% (SE = 11.0) and did not differ from that of control fish (100%) in the laboratory experiment. Tag retention was 89% (SE = 4.6) for Creek Chub and 100% for Northern Pearl Dace in the laboratory. The p-Chip microtransponder performed well during the CMR field study, with tag retention for Northern Pearl Dace at 94% across 374 days. Conclusion Our results suggest that the p-Chip microtransponder minimally affected small-bodied fish survival and had high tag retention in both the laboratory and field studies. Thus, the p-Chip microtransponder tag may be appropriate for use in small-bodied fishes when individual identification is needed in a CMR study.

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

Extracting the DBH of Moso Bamboo Forests Using LiDAR: Parameter Optimization and Accuracy Evaluation

The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Cultural heritage characteristics and damage analysis based on multidimensional data fusion and HBIM–taking the former residence of HSBC bank in Xiamen, China as an example

In the context of digitalizing cultural heritage, HBIM is garnering increasing attention from heritage conservationists for its advantages. In conservation practices for cultural heritage, expressing regional characteristics and documenting in HBIM has become increasingly crucial, and these needs are inextricably linked to the need for models with a high level of detail (LOD). This paper presents a combination of Terrestrial Laser Scanning surveys, UAV photogrammetry, and handheld laser scanning to scan cultural heritage. The scan data obtained from multiple digital surveys serves as the foundation for modelling HBIM. For the regional characteristics, the HBIM model was built with detailed geometric shapes, configurations, materials, and colors of each element, as well as complex patterns and damage using the parametric models and mesh models. The information of the building and its components stored in the HBIM model. Consequently, obtained a highly detailed HBIM that not only captures the intricate details of the ideal state and real-world damage and degradation with minimal error but also serves as a repository for a multitude of information types. It has been concluded that a high LOD is essential to representing the regional geometric feature of a building and recording the damage of cultural heritage.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.