3D Point Cloud Research Articles

Research on the Identification of Rock Mass Structural Planes and Extraction of Dominant Orientations Based on 3D Point Cloud

The different spatial distribution forms of rock mass structural planes create weak zones in the rock mass, which is also a key factor in controlling rock mass stability. Accurately and efficiently identifying rock mass structural planes and obtaining their dominant orientations is critical for rock mass engineering design and construction. Traditional surveying methods for high and steep rock mass structural planes pose high safety risks, offer limited data, and make comprehensive statistical analysis difficult. This paper utilizes complex rock mass surface 3D point cloud data obtained through 3D laser scanning technology and uses the Hough space transform method to calculate the normal vectors of the 3D point cloud. Based on the difference in normal vectors and surface variation, region growing segmentation is applied to identify and extract rock mass structural planes. Additionally, the fast search and density peak clustering method (CFSFDP) is used for clustering analysis of the rock mass structural planes to obtain dominant orientations. This method was applied to a highway’s high and steep rock slope, successfully identifying 281 structural planes and two sets of dominant structural planes. The orientation of the dominant structural planes identified through RocScience Dips 7.0 analysis showed a deviation of no more than ±3°, complying with engineering standards. The research results offer a feasible solution for the identification of high and steep rock mass structural planes and the extraction of the orientation of dominant structural planes.

Research on the Laser Scattering Characteristics of Three-Dimensional Imaging Based on Electro–Optical Crystal Modulation

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro–optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO–Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles. Finally, according to the fitted scattering characteristic model, we inverted the target imaging gray map. We used the laser 3D imaging restoration principle to reconstruct the 3D point cloud of the target to realize the laser 3D imaging of the target.

Immersive remote telerobotics: foveated unicasting and remote visualization for intuitive interaction

Abstract Precise and efficient performance in remote robotic teleoperation relies on intuitive interaction. This requires both accurate control actions and complete perception (vision, haptic, and other sensory feedback) of the remote environment. Especially in immersive remote teleoperation, the complete perception of remote environments in 3D allows operators to gain improved situational awareness. Color and Depth (RGB-D) cameras capture remote environments as dense 3D point clouds for real-time visualization. However, providing enough situational awareness needs fast, high-quality data transmission from acquisition to virtual reality rendering. Unfortunately, dense point-cloud data can suffer from network delays and limits, impacting the teleoperator’s situational awareness. Understanding how the human eye works can help mitigate these challenges. This paper introduces a solution by implementing foveation, mimicking the human eye’s focus by smartly sampling and rendering dense point clouds for an intuitive remote teleoperation interface. This provides high resolution in the user’s central field, which gradually reduces toward the edges. However, this systematic visualization approach in the peripheral vision may benefit or risk losing information and burdening the user’s cognitive load. This work investigates these advantages and drawbacks through an experimental study and describes the overall system, with its software, hardware, and communication framework. This will show significant enhancements in both latency and throughput, surpassing 60% and 40% improvements in both aspects when compared with state-of-the-art research works. A user study reveals that the framework has minimal impact on the user’s visual quality of experience while helping to reduce the error rate significantly. Further, a 50% reduction in task execution time highlights the benefits of the proposed framework in immersive remote telerobotics applications.

MSSA: Multi-Representation Semantics-Augmented Set Abstraction for 3D Object Detection

Accurate recognition and localization of 3D objects is a fundamental research problem in 3D computer vision. Benefiting from transformation-free point cloud processing and flexible receptive fields, point-based methods have become accurate in 3D point cloud modeling, but still fall behind voxel-based competitors in 3D detection. We observe that the set abstraction module, commonly utilized by point-based methods for downsampling points, tends to retain excessive irrelevant background information, thus hindering the effective learning of features for object detection tasks. To address this issue, we propose MSSA, a Multi-representation Semantics-augmented Set Abstraction for 3D object detection. Specifically, we first design a backbone network to encode different representation features of point clouds, which extracts point-wise features through PointNet to preserve fine-grained geometric structure features, and adopts VoxelNet to extract voxel features and BEV features to enhance the semantic features of key points. Second, to efficiently fuse different representation features of keypoints, we propose a Point feature-guided Voxel feature and BEV feature fusion (PVB-Fusion) module to adaptively fuse multi-representation features and remove noise. At last, a novel Multi-representation Semantic-guided Farthest Point Sampling (MS-FPS) algorithm is designed to help set abstraction modules progressively downsample point clouds, thereby improving instance recall and detection performance with more important foreground points. We evaluate MSSA on the widely used KITTI dataset and the more challenging nuScenes dataset. Experimental results show that compared to PointRCNN, our method improves the AP of “moderate” level for three classes of objects by 7.02%, 6.76%, and 5.44%, respectively. Compared to the advanced point-voxel-based method PV-RCNN, our method improves the AP of “moderate” level by 1.23%, 2.84%, and 0.55% for the three classes, respectively.

Graph-based robust 3D point cloud map merging approach for large scale

Graph-based robust 3D point cloud map merging approach for large scale

A Calculating Method for the Height of Multi-Type Buildings Based on 3D Point Cloud

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method’s effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method’s potential for broader application in tunnel blasting assessments.

3D coherent single shot lidar imaging beyond coherence length

Advancements in remote sensing and autonomous vehicle technologies made lidars equally important for unmanned objects alongside cameras. Therefore, precise 3D lidar imaging and point cloud generation have become important subjects. Although existing coherent lidar technologies provide precise imaging results, the spectral linewidth of the laser sources becomes a key limitation over long distances as it defines the maximum detection range. Here, we present long-distance 3D lidar imaging which removes the coherence length limitations and therefore the necessity of high-coherence laser sources. Mainly, we generate optical sidebands, by modulating a continuous wave (CW) laser source with multiple radio-frequency (RF) tones. Then, using our own post-processing and triangulation methods, we use the relative phase changes between the sidebands which are free from laser phase noise to determine the target distance. We prove that the multi-tone coherent Lidar technique can perform precise 3D imaging and point cloud generation of various targets at sub-10pW optical power reception and distances up to ∼12× beyond the coherence length of the CW laser employed in the lidar architecture. Overall, it is demonstrated that coherence length restriction is removed by the suggested method, which makes precise long-distance 3D lidar imaging possible, particularly for applications such as spacecraft and aerial coherent lidars.

Comprehensive Analysis of Phenotypic Traits in Chinese Cabbage Using 3D Point Cloud Technology

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.

Airborne LiDAR Point Cloud Classification Using Ensemble Learning for DEM Generation

Airborne laser scanning (ALS) point clouds have emerged as a predominant data source for the generation of digital elevation models (DEM) in recent years. Traditionally, the generation of DEM using ALS point clouds involves the steps of point cloud classification or ground point filtering to extract ground points and labor-intensive post-processing to correct the misclassified ground points. The current deep learning techniques leverage the ability of geometric recognition for ground point classification. However, the deep learning classifiers are generally trained using 3D point clouds with simple geometric terrains, which decrease the performance of model inferencing. In this study, a point-based deep learning model with boosting ensemble learning and a set of geometric features as the model inputs is proposed. With the ensemble learning strategy, this study integrates specialized ground point classifiers designed for different terrains to boost classification robustness and accuracy. In experiments, ALS point clouds containing various terrains were used to evaluate the feasibility of the proposed method. The results demonstrated that the proposed method can improve the point cloud classification and the quality of generated DEMs. The classification accuracy and F1 score are improved from 80.9% to 92.2%, and 82.2% to 94.2%, respectively, by using the proposed methods. In addition, the DEM generation error, in terms of mean squared error (RMSE), is reduced from 0.318–1.362 m to 0.273–1.032 m by using the proposed ensemble learning.

A Unified Virtual Model for Real-Time Visualization and Diagnosis in Architectural Heritage Conservation

The aim of this paper is to propose a workflow for the real-time visualization of virtual environments that supports diagnostic tasks in heritage buildings. The approach integrates data from terrestrial laser scanning (3D point clouds and meshes), along with panoramic and thermal images, into a unified virtual model. Additionally, the methodology incorporates several post-processing stages designed to enhance the user experience in visualizing both the building and its associated damage. The methodology was tested on the Medieval Templar Church of Vera Cruz in Segovia, utilizing a combination of visible and infrared data, along with manually prepared damage maps. The project results demonstrate that the use of a hybrid digital model—combining 3D point clouds, polygonal meshes, and panoramic images—is highly effective for real-time rendering, providing detailed visualization while maintaining adaptability for mobile devices with limited computational power.

Adversarial Unsupervised Domain Adaptation for 3D Semantic Segmentation with 2D Image Fusion of Dense Depth

AbstractUnsupervised domain adaptation (UDA) is increasingly used for 3D point cloud semantic segmentation tasks due to its ability to address the issue of missing labels for new domains. However, most existing unsupervised domain adaptation methods focus only on uni‐modal data and are rarely applied to multi‐modal data. Therefore, we propose a cross‐modal UDA on multi‐modal datasets that contain 3D point clouds and 2D images for 3D Semantic Segmentation. Specifically, we first propose a Dual discriminator‐based Domain Adaptation (Dd‐bDA) module to enhance the adaptability of different domains. Second, given that the robustness of depth information to domain shifts can provide more details for semantic segmentation, we further employ a Dense depth Feature Fusion (DdFF) module to extract image features with rich depth cues. We evaluate our model in four unsupervised domain adaptation scenarios, i.e., dataset‐to‐dataset (A2D2 → SemanticKITTI), Day‐to‐Night, country‐to‐country (USA → Singapore), and synthetic‐to‐real (VirtualKITTI → SemanticKITTI). In all settings, the experimental results achieve significant improvements and surpass state‐of‐the‐art models.

Underwater point cloud transmission framework: hybrid encoder implementation based on CNN and transformer

Abstract In underwater multi-robot systems, 3D point cloud data generated by sonar and depth sensors facilitates the execution of more complex collaborative tasks among robots, which partly rely on efficient data transmission. In this work, we propose a hybrid encoder framework based on convolutional neural network and Transformer for underwater point cloud transmission, aimed at dealing with large-scale point cloud data. Our encoder allows setting lower compression rate on regions or objects of interest for semantic point clouds, preserving crucial information in the point cloud. For underwater acoustic communication, we employ orthogonal frequency division multiplexing combined with deep joint source-channel coding for transmission to enhance the system’s error-resilience. Compared to state-of-the-art methods in the simulation experiments, our end-to-end framework achieves considerable compression performance while eliminating certain cliff and leveling effects, also demonstrating robustness even with changing channels.

DSGI‐Net: Density‐based Selective Grouping Point Cloud Learning Network for Indoor Scene

AbstractIndoor scene point clouds exhibit diverse distributions and varying levels of sparsity, characterized by more intricate geometry and occlusion compared to outdoor scenes or individual objects. Despite recent advancements in 3D point cloud analysis introducing various network architectures, there remains a lack of frameworks tailored to the unique attributes of indoor scenarios. To address this, we propose DSGI‐Net, a novel indoor scene point cloud learning network that can be integrated into existing models. The key innovation of this work is selectively grouping more informative neighbor points in sparse regions and promoting semantic consistency of the local area where different instances are in proximity but belong to distinct categories. Furthermore, our method encodes both semantic and spatial relationships between points in local regions to reduce the loss of local geometric details. Extensive experiments on the ScanNetv2, SUN RGB‐D, and S3DIS indoor scene benchmarks demonstrate that our method is straightforward yet effective.

Point‐AGM : Attention Guided Masked Auto‐Encoder for Joint Self‐supervised Learning on Point Clouds

AbstractMasked point modeling (MPM) has gained considerable attention in self‐supervised learning for 3D point clouds. While existing self‐supervised methods have progressed in learning from point clouds, we aim to address their limitation of capturing high‐level semantics through our novel attention‐guided masking framework, Point‐AGM. Our approach introduces an attention‐guided masking mechanism that selectively masks low‐attended regions, enabling the model to concentrate on reconstructing more critical areas and addressing the limitations of random and block masking strategies. Furthermore, we exploit the inherent advantages of the teacher‐student network to enable cross‐view contrastive learning on augmented dual‐view point clouds, enforcing consistency between complete and partially masked views of the same 3D shape in the feature space. This unified framework leverages the complementary strengths of masked point modeling, attention‐guided masking, and contrastive learning for robust representation learning. Extensive experiments have shown the effectiveness of our approach and its well‐transferable performance across various downstream tasks. Specifically, our model achieves an accuracy of 94.12% on ModelNet40 and 87.16% on the PB‐T50‐RS setting of ScanObjectNN, outperforming other self‐supervised learning methods.

TreeCompR: Tree competition indices for inventory data and 3D point clouds

Abstract In times of more frequent global change‐type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices (CI) leave users with a difficult choice, as no single competition index has proven universally superior. To help users with the choice and computation of appropriate indices, we present the open‐source TreeCompR package, which handles 3D point clouds and classical forest inventory data, enabling the calculation of both innovative point cloud‐based indices and traditional distance‐dependent indices. It serves as a centralized platform for exploring and comparing different CIs, allowing users to test and select the most suitable CI for their specific research questions within a common interface. To evaluate the package, we used TreeCompR to quantify the competition situation of 307 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. The compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well‐structured output, our package can easily be integrated into existing data‐analysis workflows both for 3D point cloud and classical forest inventory data.

CPH-Fmnet: An Optimized Deep Learning Model for Multi-View Stereo and Parameter Extraction in Complex Forest Scenes

The three-dimensional reconstruction of forests is crucial in remote sensing technology, ecological monitoring, and forestry management, as it yields precise forest structure and tree parameters, providing essential data support for forest resource management, evaluation, and sustainable development. Nevertheless, forest 3D reconstruction now encounters obstacles including higher equipment costs, reduced data collection efficiency, and complex data processing. This work introduces a unique deep learning model, CPH-Fmnet, designed to enhance the accuracy and efficiency of 3D reconstruction in intricate forest environments. CPH-Fmnet enhances the FPN Encoder-Decoder Architecture by meticulously incorporating the Channel Attention Mechanism (CA), Path Aggregation Module (PA), and High-Level Feature Selection Module (HFS), alongside the integration of the pre-trained Vision Transformer (ViT), thereby significantly improving the model’s global feature extraction and local detail reconstruction abilities. We selected three representative sample plots in Haidian District, Beijing, China, as the study area and took forest stand sequence photos with an iPhone for the research. Comparative experiments with the conventional SfM + MVS and MVSFormer models, along with comprehensive parameter extraction and ablation studies, substantiated the enhanced efficacy of the proposed CPH-Fmnet model in addressing difficult circumstances such as intricate occlusions, poorly textured areas, and variations in lighting. The test results show that the model does better on a number of evaluation criteria. It has an RMSE of 1.353, an MAE of only 5.1%, an r value of 1.190, and a forest reconstruction rate of 100%, all of which are better than current methods. Furthermore, the model produced a more compact and precise 3D point cloud while accurately determining the properties of the forest trees. The findings indicate that CPH-Fmnet offers an innovative approach for forest resource management and ecological monitoring, characterized by cheap cost, high accuracy, and high efficiency.

Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations

This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors.

Mapping predicted biomass in cereal rye using 3D imaging and geostatistics

Abstract Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.