Three-dimensional Point Cloud Data Research Articles

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Application of UAV 3D Point Cloud Data in Highway Slope Disaster Investigation

The safety and stability of highway slopes have always been critical concerns in highway management and maintenance. However, mountain road slopes, characterized by their small scale, large quantity, and complex geological conditions, pose challenges for effective deformation detection, putting significant pressure on highway management and maintenance. To address the difficulty in identifying hidden hazards of highway slopes in mountainous areas, this study proposes the use of unmanned aerial vehicle (UAV) closed-range photogrammetry technology for slope inspection. The M3C2 (Multiscale Model-to-Model Cloud Comparison) algorithm is employed to accurately calculate differences in multi-phase three-dimensional point cloud data, enabling the detection of overall slope deformation with centimeter-level precision. Field verification conducted on a highway slope in Qingtian County, Zhejiang Province, demonstrated the applicability of UAV 3D point cloud data in investigating highway slope hazards, thereby enhancing inspection efficiency and quality and providing technical support for highway management and maintenance.

Research on slope algorithm based laser welding guidance technology for 3D vision inspection of thin-walled parts

Visual guidance technology advances quickly and so do the technical requirements for weld trajectory, which needs to fulfill performance indices for high precision, anti-interference and quick processing. In this study, the approach of getting 3D point cloud data by directly scanning the workpiece to be welded with a 3D scanner is used because it is more precise and reliable than processing gray-layer data obtained from a depth camera, turning it into a 3D point cloud. The weld boundary is retrieved by slope calculation for the butt weld after preprocessing the three-dimensional point cloud data (cut, down-sampling, and abnormal value removal). The center point is then determined. After that, a welding trajectory is produced by fitting the center point, which is transmitted back to the actuator to guide its welding work and realize the identification and tracking of the weld.

Measuring the profile of aircraft engine blades using spectral confocal sensors

The geometric parameters of aircraft engine blades need to be precisely measured to ensure the quality of the blades for the normal operation of aircraft engines. This study aims to address the challenges of existing measurement systems in balancing efficiency, accuracy, and completeness. Additionally, it aims to enhance the accuracy and robustness of the algorithm for extracting blade profile characteristic parameters. In this paper, a spectral confocal sensor is employed to establish a blade profile measurement system. The design includes a probe sampling strategy, and a standard ball is used to calibrate the sensor probe’s light emission direction and the precise rotation center of the turntable. The paper proposes the use of methods such as partition search algorithm, binary search, and curvature segmentation to process point cloud data of blade body and tenon. We have conducted experimental measurements on the blades of an aircraft engine. The acquired three-dimensional point cloud data of multiple sets of blade cross-sections and dovetail sections were processed. After calculation, the maximum measurement errors for chord length, maximum blade thickness, tenon width, bottom height, top angle, and bottom angle are −0.0036 mm, −0.00721 mm, −0.0102 mm, −0.00928 mm, 0.0086°, and −0.0058°, respectively. This process validates the effectiveness of the proposed method and has high measurement accuracy. Compared with the CMM method, this method is more accurate in measuring small pits and large curvature micro surfaces, with higher measurement integrity and higher measurement efficiency.

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.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

Feature extraction and representation learning of 3D point cloud data

Three-dimensional point cloud data serves as a critical source of information in various real-world application domains, such as computer vision, robotics, geographic information systems, and medical image processing. Due to the discrete and unordered nature of point clouds, applying 2D image feature extractors directly to the extraction of 3D point cloud features is challenging. Therefore, we propose a novel variational feature component extraction method called PointFEA. This paper aims to research and propose a series of methods to enhance the feature extraction and representation learning of 3D point cloud data. Firstly, in terms of feature extraction, local neighborhood encoding is combined with the local latent representation of point clouds to obtain more correlated point cloud features. Secondly, in the domain of point cloud representation learning, the multi-scale representation learning method maps point cloud data into a high-dimensional space to better capture critical features and adapt to different granularities of point cloud data. Lastly, features of different dimensions are input into a cross-fusion transformer to obtain local attention coefficients. We validate our methods on commonly used point cloud datasets, and the experiments demonstrate the effectiveness of our approach, achieving accuracies of 94.8% on ModelNet40 and 89.1% on ScanObjectNN.

Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization

To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs’ contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system’s efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development.

Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments.

In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.

PointNAC: Copula-Based Point Cloud Semantic Segmentation Network

Three-dimensional point cloud data generally contain complex scene information and diversified category structures. Existing point cloud semantic segmentation networks tend to learn feature information between sampled center points and their neighboring points, while ignoring the scale and structural information of the spatial context of the sampled center points. To address these issues, this paper introduces PointNAC (PointNet based on normal vector and attention copula feature enhancement), a network designed for point cloud semantic segmentation in large-scale complex scenes, which consists of the following two main modules: (1) The local stereoscopic feature-encoding module: this feature-encoding process incorporates distance, normal vectors, and angles calculated based on the cosine theorem, enabling the network to learn not only the spatial positional information of the point cloud but also the spatial scale and geometric structure; and (2) the copula-based similarity feature enhancement module. Based on the stereoscopic feature information, this module analyzes the correlation among points in the local neighborhood. It enhances the features of positively correlated points while leaving the features of negatively correlated points unchanged. By combining these enhancements, it effectively enhances the feature saliency within the same class and the feature distinctiveness between different classes. The experimental results show that PointNAC achieved an overall accuracy (OA) of 90.9% and a mean intersection over union (MIoU) of 67.4% on the S3DIS dataset. And on the Vaihingen dataset, PointNAC achieved an overall accuracy (OA) of 85.9% and an average F1 score of 70.6%. Compared to the segmentation results of other network models on public datasets, our algorithm demonstrates good generalization and segmentation capabilities.

An AI-Based Workflow for Fast Registration of UAV-Produced 3D Point Clouds

The integration of structure from motion (SFM) and unmanned aerial vehicle (UAV) technologies has allowed for the generation of very high-resolution three-dimensional (3D) point cloud data (up to millimeters) to detect and monitor surface changes. However, a bottleneck still exists in accurately and rapidly registering the point clouds at different times. The existing point cloud registration algorithms, such as the Iterative Closest Point (ICP) and the Fast Global Registration (FGR) method, were mainly developed for the registration of small and static point cloud data, and do not perform well when dealing with large point cloud data with potential changes over time. In particular, registering large data is computationally expensive, and the inclusion of changing objects reduces the accuracy of the registration. In this paper, we develop an AI-based workflow to ensure high-quality registration of the point clouds generated using UAV-collected photos. We first detect stable objects from the ortho-photo produced by the same set of UAV-collected photos to segment the point clouds of these objects. Registration is then performed only on the partial data with these stable objects. The application of this workflow using the UAV data collected from three erosion plots at the East Tennessee Research and Education Center indicates that our workflow outperforms the existing algorithms in both computational speed and accuracy. This AI-based workflow significantly improves computational efficiency and avoids the impact of changing objects for the registration of large point cloud data.

Study on the Measurement Method of Wheat Volume Based on Binocular Structured Light

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.

Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)

The analysis of three-dimensional point cloud data is becoming one of the most used approaches to assess instabilities processes affecting rock slopes. With the increased collection of point cloud data, there is an increasing demand for rapid computational point cloud segmentation techniques to format data for rock fall risk analysis. However, there is a scarcity of semantic segmentation research focusing on geological data and the practical issues they pose. In this study, we propose a novel application to automatically define the spatial extent and intensity of weathered rock slope areas. The studied rock slopes are in Hegra Archaeological Site (al-Hijr / Madā ͐ in Ṣāliḥ), the first UNESCO World Heritage area inscribed in the Kingdom of Saudi Arabia (KSA) in 2008. Here, cavernous weathering processes, with honeycombs and tafoni features, affect the carved rock slope and increase the rockfall hazard. With the spread and improvement of close-range methods, such as Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle Digital Photogrammetry (UAV-DP), it is possible to obtain fast and accurate high-resolution point cloud data of the studied scenario, i.e., rock mass. This paper presents the automated mapping results obtained by a combination of explainable artificial intelligence (XAI) algorithms and three-dimensional geometric analysis of a point cloud using the geometric feature tools available for free on CloudCompare (CC) open-source software. Three-dimensional data were obtained using the UAV-DP technique. The proposed workflow allows, using random forest modelling (RF), to quantify the cavernous weathering extent and estimate the relative intensity on the rock slope, faster than using traditional time-consuming manual segmentation processes. The RF model was able to accurately forecast the cavernous weathering regions (accuracy of up to 85%), with a slight propensity to underpredict, as indicated by the somewhat low recall (∼ 63%), and a limited number of false positives, as indicated by the high precision (up to 84%). This approach is easily reproducible at low cost in terms of computational, human, and financial resources. The results highlight the reliability of the proposed method for the preliminary assessment of cavernous erosional features with the aim of future assessment, monitoring, and deeper analysis of rock-carved slopes.

Study on the Estimation of Concrete Defects Volume on Dam Body Surface

To maintain the safety and functionality of dams over the long term, it is necessary to make inspections more labor-saving and efficient using the latest technology and to improve the sophistication of inspections based on data. Although dam inspections cover a wide range of items, this study focuses on the continuous monitoring of popouts, a phenomenon of concrete deterioration occurring on the surface of a dam body. It is difficult to predict whether a popout will occur from information on the body surface of the dam, owing to the generation mechanism of the popout. The number of popouts was monitored over time; however, no examples of shape changes were monitored over time. Advancements in various digital technologies are required to accurately evaluate changes in the dam body's surface over time; therefore, in this study, three-dimensional (3D) point-cloud data is created by the Structure from Motion (SfM) from images captured by a Unmanned Aerial Vehicle (UAV) of the concrete defect area due to the popout in an arch dam in the Tohoku region of Japan. The volume of concrete defects of a popout in each of two different periods was calculated by estimating the plane shape of the surface of the dam body. In addition, the shapes of two popouts were compared to confirm the possibility of predictive signs of change.

Body weight estimation of beef cattle with 3D deep learning model: PointNet++

It is necessary to measure the live body weight (BW) of beef cattle to assess their health and nutritional degree. Instead of continual manual measuring in holding frames or crates currently used in the breeding industry, this paper has proposed a novel non-contact BW prediction with 3D deep learning model PointNet++. The three-dimensional (3D) point cloud data (PCD) of the whole contour surface of a beef cattle is proximally obtained by a light detection and ranging (LiDAR) sensor. To preserve the quality of the silhouette, the fusion of classical PCD preprocessing is employed. To better the accuracy of manual selection to the position of body conformation, the data extension of PCD and dataset annotations expressed by the feature regions are employed. The PCD part segmentation of deep learning models is validated and redefined with datasets of beef cattle, where the PointNet++ model with the highest accuracy of 95.1% is finally redesigned. With the advantages of being suitable for most beef breeds for the classical Johnson’s live BW prediction, the Gaussian curvature and principal curvature of contour points of feature regions are calculated to extract the body measurement points of body size, and the body length (BL) and chest girth (CG) are calculated and fit-calibrated. With the experimentation of 100 cattle in breeding farms, the results of live BW prediction have shown that the mean absolute percentage error (MAPE) is 3.2%, the root mean square error (RMSE) is 10.2 kg. The effective and practical resolution of BW estimation can improve the weighing process and animal welfare in large-scale breeding farms, and the real silhouette dataset of large animals can help the automated recognition and classification of body conformation.

Distributed Optimization Based on Graph Filter for Ensuring Invariant Simplification of High-Volume Point Cloud

In many consumer electronic applications, three-dimensional (3D) point cloud data (PCD) is commonly generated in data-driven models. The expensive burden of computation and storage would be brought out, as the point number reaches billions in the large-scale augmented or virtual reality models. Based on the concept of graph signal, a new PCD simplification approach is developed to handle the high-volume PCD and develop a feature-controllable mechanism. In particular, PCD simplification is modeled as an optimization problem with the hybridization of contour and planar feature items. To reduce the complexity of computation, invariant decomposition is proposed, and distributed optimization is developed for the feature controllable PCD simplification. The self-adoption scheme is adopted to improve the convergence rate. Verification experiments and applications on different scenarios demonstrate the effectiveness and feasibility of the proposed simplification approach.

High-Accuracy Filtering of Forest Scenes Based on Full-Waveform LiDAR Data and Hyperspectral Images

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m.

Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data

Pavement roughness serves as a crucial indicator for evaluating road performance. However, traditional measurement methods, such as laser detection vehicles, are limited to providing roughness values for a single profile, failing to capture the overall pavement condition comprehensively. To address this limitation, this study utilized high-precision light detection and ranging technology (LiDAR) to acquire three-dimensional point cloud data for a 25 km road section in Shanghai. Road elevations were extracted from different lateral survey lines. Subsequently, variance analysis and the Kruskal–Wallis non-parametric test were conducted to evaluate the differences in the lateral distribution and longitudinal variability of the pavement roughness. The findings revealed significant differences in the international roughness index (IRI) among the survey lines within the road section. Moreover, the observed variations in the lateral distribution of pavement roughness were influenced by the characteristics of the road section itself. Roads exhibiting discrete roughness patterns displayed a higher likelihood of significant detection disparities. Additionally, it was discovered that the discrepancy between the detection length and the actual road length introduced volatility in repeated detection results, necessitating a limitation of this discrepancy to 30 m. Consequently, it has been recommended to consider the lateral distribution of pavement roughness and to regulate the detection length in road performance evaluations to enhance reliability and facilitate more accurate maintenance decision making. The study highlights the importance of incorporating comprehensive assessment approaches for pavement roughness in road management practices.

Building Tilt Scanning Measurement Based on Binary Linear Regression Analysis

Abstract With the development of modern surveying and mapping technology, 3D laser scanners are increasingly used for building dip scanning. Building inclination characteristics are analyzed using three-dimensional point cloud data and binary linear regression. It can easily and intuitively obtain the inclination of the building. This method extracted the building inclination Angle from the point, line, plane, surface, and other angles. Then, a matching method based on two dimensions and surfaces is proposed, with a strong anti-noise ability. An optimal matching algorithm of structural plane and surface based on quadratic linear regression is proposed. Experiments prove the anti-interference ability and accuracy of this method. It is found that the binary linear regression fitting method can effectively identify the noise signal when there is a lot of noise interference in the point cloud data. It has good anti-interference ability.

Experiment and Application of NATM Tunnel Deformation Monitoring Based on 3D Laser Scanning

In recent years, 3D laser scanning technology has been applied to tunnel engineering. Although more intelligent than traditional measurement technology, it is still challenging to estimate the real-time deformation of NATM tunnel excavation from laser detection and ranging point clouds. To further improve the measurement accuracy of 3D laser scanning technology in the tunnel construction process, this paper proposes an improved Kriging filtering algorithm. Considering the spatial correlation of the described object, the optimization method of point cloud grid filtering is studied. By analyzing the full-space deformation field of the tunnel lining, the deformation information of the measuring points on the surface of the tunnel lining is extracted. Based on the actual project, through the on-site monitoring comparison test, the three-dimensional laser point cloud data are grid processed and analyzed, and the deformation data obtained from the test are compared with the data measured by traditional methods. The experimental results show that the Kriging filtering algorithm can not only efficiently identify and extract the tunnel profile visualization data but also efficiently and accurately obtain the tunnel deformation. The measurement results obtained by using the proposed technology are in good agreement with those obtained by using traditional monitoring methods. Therefore, tunnel deformation monitoring based on 3D laser scanning technology can better reflect the evolution of the tunnel full-space deformation field under certain environmental conditions and can provide an effective safety warning for tunnel construction.