3D Point Cloud Research Articles

Non-Contact Human Body Dimension Estimation Methods Based on Deep Learning: A Critical Analysis

With the advancement of information technology, Human Body Dimension Estimation (HBDE) is moving towards digitalization and non-contact methods. This paper critically reviews non-contact HBDE methods based on deep learning, primarily covering image-based methods, the Adaptive Body Structure Segmentation (ABSS) algorithm with height and weight measurements, and 3D meshes or point cloud data methods. Convolutional Neural Networks (CNNs) excel in image measurement, while the ABSS algorithm with adaptive segmentation achieves high accuracy. Recent studies, such as the Grey Wolf Optimizer-Elman Neural Network (GBWO-ENN) model, have significantly improved prediction accuracy by optimizing neural network structures. Additionally, 3D point cloud data combined with the Skinned Multi-Person Linear (SMPL) model has proven effective in complex scenarios. This paper summarizes the performance of various methods, analyzes common datasets, and explores future development directions for non-contact HBDE. Despite significant progress, this field still faces challenges such as data quality, scarcity of extreme samples, and privacy issues. Future research should focus on building high-quality datasets and developing lightweight measurement networks to meet practical application needs.

Robust and smooth Couinaud segmentation via anatomical structure-guided point-voxel network

Precise Couinaud segmentation from preoperative liver computed tomography (CT) is crucial for surgical planning and lesion examination. However, this task is challenging as it is defined based on vessel structures, and there is no intensity contrast between adjacent Couinaud segments in CT images. To solve this challenge, we design a multi-scale point-voxel fusion framework, which can more effectively model the spatial relationship of points and the semantic information of the image, producing robust and smooth Couinaud segmentations. Specifically, we first segment the liver and vessels from the CT image and generate 3D liver point clouds and voxel grids embedded with the vessel structure. Then, our method with two input-specific branches extracts complementary feature representations from points and voxels, respectively. The local attention module adaptively fuses features from the two branches at different scales to balance the contribution of different branches in learning more discriminative features. Furthermore, we propose a novel distance loss at the feature level to make the features in the segment more compact, thereby improving the certainty of segmentation between segments. Our experimental results on three public liver datasets demonstrate that our proposed method outperforms several state-of-the-art methods by large margins. Specifically, in out-of-distribution (OOD) testing of LiTS dataset, our method exceeded the voxel-based 3D UNet by approximately 20% in Dice score, and outperformed the point-based PointNet2Plus by approximately 8% in Dice score. Our code and manual annotations of the public datasets presented in this paper are available online: https://github.com/xukun-zhang/Couinaud-Segmentation.

Assessing grapevine water status through fusion of hyperspectral imaging and 3D point clouds

Mild to moderate and timely water deficit is desirable in grape production to optimize fruit quality for winemaking. It is crucial to develop robust and rapid approaches to assess grapevine water stress for scheduling deficit irrigation. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are restricted in field applications. The varied leaf orientations can significantly affect how light interacts with the plant, ultimately influencing the reflectance properties. This study focused on developing an approach for detecting grapevine water status using HSI and 3D data. Leaf orientation parameters derived from 3D point clouds were integrated with spectral signatures to address the spectral variance caused by variations in leaf orientation. A water status assessment model was developed based on multiblock partial least squares (MBPLS) to estimate midday leaf water potential (ΨL) using spectral signatures and leaf orientation parameters. HSI and 3D point clouds of selected leaves were captured simultaneously in a vineyard during the 2021 growing season, and ΨL was measured as the ground truth to assess the model performance. Mean spectral reflectance was derived from the hyperspectral images, while leaf orientation parameters were extracted from 3D point cloud data. The dataset was split randomly into 70% training/calibration and 30% test datasets. The test result shows that the model estimated the ΨL with R2 = 0.89, RMSE=0.12 MPa and MAE=0.09 MPa. The leaf orientation parameters derived from 3D point clouds had a contribution of 6.25% in estimating ΨL. Moreover, it acted as an enhancing component that explained the spectral variance caused by variations in leaf orientation and improved the interpretation of the underlying relationship between spectral reflectance and vine water status.

Hyperbolic prototype rectification for few-shot 3D point cloud classification

Few-shot point cloud classification is a challenging task in 3D computer vision and has received widespread attention from researchers. Most of the works on deep learning models rely heavily on Euclidean spatial metrics. However, point cloud objects often have complex non-Euclidean geometric structures, with underlying inter/intra-class hierarchical structures, which are difficult to capture by current Euclidean-based deep learning models. Moreover, due to the lack of training samples, many few-shot learning methods often suffer from the overfitting problem. Given the Hyperbolic metric of non-Euclidean geometry offering hierarchical structural prior, as we assume to be able to assist FSL task, we propose Hyperbolic Prototype Rectification (HPR) for few-shot point cloud classification, without requiring extra learnable parameter. Firstly, point clouds are embedded into hyperbolic space to better describe hierarchical similarity relationships in data. Secondly, the HPR utilizes hyperbolic spatial and distributional information to enhance the feature representation and improve the generalization capability, with more appropriate hyperbolic prototypes. The few-shot classification experiments and further ablation studies conducted on widely used point cloud datasets demonstrate the effectiveness of our method. On the real-world ScanObjectNN(-PB) datasets, the average classification accuracy outperforms the SOTA method by 2.08%(0.66%), respectively, indicating that the proposed HPR has great generalization capability and strong robustness against perturbed data. Our code is available at: https://github.com/Jonathan-UCAS/HPR.

3D Point Cloud Semantic Segmentation based on Multi-scale Dense Nested Networks

Aiming at the problem that the relationship between geometric features and semantic features is ignored in the point cloud data downsampling process, which leads to inaccurate segmentation of object boundaries and structural details, this paper proposes a 3D point cloud semantic segmentation network based on multi-scale dense nested type. Firstly, a dense nested network architecture is constructed by nesting multiple multi-scale feature fusion modules to fuse multi-scale features of different directions between encoder-decoder paths, so as to effectively propagate local ge-ometric context information and enhance the ability of cross-scale information interaction. Secondly, a local feature aggregation unit is constructed in the multi-scale feature fusion module, which strengthens the structural awareness within the local point set based on graph convolution and attention mechanism, and promotes the complementarity of local geometric features and abstract semantic information. Then, the cross-layer multi-loss supervision module is combined to further optimize the multi-scale feature propagation, which makes the network training more stable and improves the point cloud segmentation accuracy. Finally, this paper verified the proposed network on the S3DIS dataset. The experimental results show that the proposed network has the mean intersection over Union of 71.2% and the overall accuracy of 88.7%, which is 1.2 and 0.7 percentage points higher than RandLA-Net, respectively, which proves that the proposed network can effectively improve the accuracy of 3D point cloud semantic segmentation.

From top to deep: An integrated multidisciplinary approach for the study of a transformative landscape of Savatra ancient city

AbstractIn this study, a combined workflow of computational methodologies is introduced to explore the transformative landscape of the ancient city of Savatra (Central Anatolia Region, Türkiye), which faces long‐term risks stemming from natural and anthropogenic threats. Emphasis was placed on regional and local scale landscape analysis, employing aerial and ground‐based remote‐sensing techniques to unravel past settlement patterns and understand the impact of environmental factors, topography and natural resources on both the location of Savatra and spatial organization of its features. On a regional scale, the influence of hydrological conditions, slope and aspect on the landscape was determined through the employment of Geographical Information System (GIS)‐based analysis of digital elevation models (DEMs). At a more local scale, the utilization of the Unmanned Aerial Systems‐derived DEM and geophysical survey helped identify potential archaeological features and also assessed the risk posed to these features. Furthermore, the incorporation of 3D GIS analysis, integrating 3D point cloud representations of the ground‐penetrating radar volume and DEM, provided essential insight into the state of preservation of the buried features. The collaborative application and joint interpretation of these methodologies yielded a wide range of clues and explanations, unravelling the complex palimpsest of past activities. This research not only serves as foundation for future studies specifically for Savatra, but also provides a preliminary remote sensing–based exploration blueprint to other yet to be studied archaeological sites.

2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing

Abstract The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and feature visualization. Hash function is well known for its high-speed and accurate performance in searching high-dimensional data, which is also the core of the proposed 2L-LSH. Specifically, the 2L-LSH algorithm adopts a two-step hash function strategy, in which the popular step divides the bounding box of the point cloud model and the second step constructs a generalized table-based data structure. The proposed 2L-LSH offers a highly efficient and accurate solution for fast neighboring points search in large-scale 3D point cloud models, making it a promising technique for various applications in the field. The proposed algorithm is compared with the well-known methods including Kd-tree and Octree; the obtained results demonstrated that the proposed method outperforms Kd-tree and Octree in terms of speed, i.e. the time consumption of kNN search can be 51.111% and 94.159% lower than Kd-tree and Octree, respectively. And the RN search time can be 54.519% and 41.840% lower than Kd-tree and Octree, respectively.

A computer-vision based framework for virtual 3D garment reconstruction

AbstractExisting 3D garment reconstruction methods are difficult to implement for online fashion design and e-commerce or special applications. This paper proposes a novel computer-vision framework for 3D garment digital reconstruction, which aims to reconstruct high-quality and realistic virtual 3D garments with fabric mechanic properties for 3D virtual try-on. The new segmentation scheme is proposed to separate the 3D garment point clouds from background points, which is suitable for 3D human shapes and is adaptive for different 3D garment models in different scenes. The new Statistical Outlier Removal algorithm and the learning-based method PointCleanNet are combined to remove noise and outliers in 3D garment point clouds, which provides high-fidelity and high-quality 3D garment point clouds. The 3D garment meshes are then reconstructed from their corresponding point clouds with a modified rolling ball algorithm. Finally, the meshes are improved and converted into physics-based virtual try-on 3D garments with fabric mechanic properties added, which enables the assessment of different body shapes with varied sizes for the same reconstructed 3D garment. Comparison experiments demonstrate that our framework achieves high-quality and realistic 3D garment reconstruction and accurate 3D virtual try-on from 2D garment images. We also demonstrate the proposed framework on a large range of various garments to show this approach has a great potential for garment future technology, such as online garment shopping, garment design and manufacturing.

Research on a 3D Point Cloud Map Learning Algorithm Based on Point Normal Constraints.

Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency.

TAMC: Textual Alignment and Masked Consistency for Open-Vocabulary 3D Scene Understanding.

Three-dimensional (3D) Scene Understanding achieves environmental perception by extracting and analyzing point cloud data with wide applications including virtual reality, robotics, etc. Previous methods align the 2D image feature from a pre-trained CLIP model and the 3D point cloud feature for the open vocabulary scene understanding ability. We believe that existing methods have the following two deficiencies: (1) the 3D feature extraction process ignores the challenges of real scenarios, i.e., point cloud data are very sparse and even incomplete; (2) the training stage lacks direct text supervision, leading to inconsistency with the inference stage. To address the first issue, we employ a Masked Consistency training policy. Specifically, during the alignment of 3D and 2D features, we mask some 3D features to force the model to understand the entire scene using only partial 3D features. For the second issue, we generate pseudo-text labels and align them with the 3D features during the training process. In particular, we first generate a description for each 2D image belonging to the same 3D scene and then use a summarization model to fuse these descriptions into a single description of the scene. Subsequently, we align 2D-3D features and 3D-text features simultaneously during training. Massive experiments demonstrate the effectiveness of our method, outperforming state-of-the-art approaches.

Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning

Advancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This paper introduces a novel nonparametric distributed (NPD) learning framework that utilizes trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone (1982) under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online.

Utilizing 3D Point Clouds and Deep Learning Technology in Multimedia Instruction for Sports Mechanics

In this paper, an optimization model based on 3D point cloud and deep learning theory is constructed, which improves the fluency and accuracy of multimedia sports operation. The model has performed well in sports mechanics teaching and proved its outstanding performance. It is found that the changes of the three kinds of point clouds have different laws, and the spherical neighborhood gradually decreases, while the adjacent neighborhood shows an upward fluctuation trend. The neighborhood of cylinder shows linear and nonlinear characteristics. The parameter curve of point cloud model fluctuates greatly, and the curve corresponding to function output value fluctuates widely, while the curve corresponding to feature extraction method fluctuates relatively little. The ability training index plays a significant role in improving the model output, the multimedia input index fluctuates well, and the teaching method index shows an approximate linear change law. The accuracy of the model is verified by data calculation method, which provides theoretical guidance for multimedia sports mechanics teaching.

Triple-Camera Rectification for Depth Estimation Sensor.

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera for creating 3D point clouds. Existing rectification methods only align two cameras, necessitating an additional rectification and remapping process to align the third camera. Moreover, these methods require multiple reference checkerboard images for calibration and aim to minimize alignment errors, but often result in rotated images when there is significant misalignment between two cameras. In contrast, the proposed method simultaneously rectifies three cameras in a single shot without unnecessary rotation. To achieve this, we designed a lab environment with checkerboard settings and obtained multiple sample images from the cameras. The optimization function, designed specifically for rectification in stereo matching, enables the simultaneous alignment of all three cameras while ensuring performance comparable to traditional methods. Experimental results with real camera samples demonstrate the benefits of the proposed method and provide a detailed analysis of unnecessary rotations in the rectified images.

MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration

Abstract 3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.

Autonomous Vehicles Traversability Mapping Fusing Semantic–Geometric in Off-Road Navigation

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process.

Automated recognition and rebar dimensional assessment of prefabricated bridge components from low-cost 3D laser scanner

Dimension quality inspections for rebar spacing and length of prefabricated bridge components are critical for subsequent efficient assembly on-site. Currently, the traditional manual inspection method is time-consuming, labor-intensive, and error-prone. The existing commercial 3D LiDAR-based methods are difficult in making balances between inspection cost, efficiency, and accuracy. Thus, the automated recognition and segmentation method of 3D point clouds acquired by a self-developed low-cost LiDAR is proposed to evaluate the rebar spacing and length of bridge prefabricated components. Due to the high-cost commercial 3D laser scanner, a spinning 2D LiDAR-based low-cost 3D laser scanner device and geometric calibration model were developed to acquire the 3D spatial point cloud. Then, two-stage automated point cloud segmentation framework is proposed with coarse extraction of the point cloud in the interest region and fine recognition of point cloud using machine learning methods, including plane segmentation and circle fitting employing random sample consensus (RANSAC) algorithm, and rebars segmentation utilizing Gaussian mixture model (GMM) and density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed system was evaluated in two prefabricated concrete columns and shows the potential for improving the quality inspection efficiency and accuracy.

Leveraging occupancy map to accelerate video-based point cloud compression

Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.

Sports action recognition algorithm based on multi-modal data recognition

The recognition of sports action is an important research subject, which is conducive to the improvement of athletes’ own level. To improve the accuracy of multi-modal data action recognition, based on the Transformer module, this study introduces a multi-head attention mechanism, fuses multi-modal data, and constructs a multi-stream structured object relationship inference network. Based on PointNet++ network and combining five different data fusion frameworks, a motion recognition model that integrates RGB data and 3D skeleton point cloud is constructed. The results showed that the Top-1 accuracy of multi-stream structured object relationship inference network was 42.5% and 42.7%, respectively, which was better than other algorithms. The accuracy of the multi-modal fusion model was improved by 15.6% and 5.1% compared with the single mode, and by 5.4% and 2.6% compared with the dual mode, which showed its superiority in the action recognition task. This showed that the fusion of multi-modal data can provide more abundant information, so as to improve the accuracy of action recognition. The accuracy of the action recognition model combining RGB data and 3D skeleton point cloud was 84.3%, 87.5%, 90.2%, 90.6% and 91.2% after the combination of different strategies, which effectively compensated for the problem of missing information in 3D skeleton point cloud and significantly improved the accuracy of action recognition. With a small amount of data, the Top-1 accuracy of the multi-stream structured object relationship inference network in this study was superior to other algorithms, showing its advantages in dealing with complex action recognition tasks. In addition, the action recognition model that fuses RGB data and 3D skeleton point cloud also achieved higher accuracy, which is better than other algorithms. This study can meet the needs of motion recognition in different scenarios and has certain reference value.

PCAlign: a general data augmentation framework for point clouds

With the advancement of 3D scanning technologies and deep learning theories, point cloud-based deep learning networks have gained considerable attention in the fields of 3D vision and computer graphics. Leveraging the rich geometric information present in 3D point clouds, these networks facilitate more accurate feature learning tasks. However, existing networks often suffer from generalization defects caused by variations in pose and inconsistent representations of training data. In this paper, we propose a novel data augmentation framework to overcome these limitations. Our approach utilizes principal component analysis (PCA) to generate four aligned copies of a point cloud. These copies are then input into a multi-channel structure, which is compatible with popular backbones of point cloud-based deep networks. Finally, the outputs of the multi-channel structure are merged to generate rotation-invariant feature learning results. Experimental evaluations demonstrate the efficacy of our framework, showcasing significant improvements in various existing point cloud-based deep learning methods. Notably, our method exhibits enhanced robustness in classification tasks, particularly when dealing with point clouds containing random pose variations and non-uniform densities. Project link: https://github.com/LAB123-tech/PCAlign.

R-C-D-F machine learning method to measure for geological structures in 3D point cloud of rock tunnel face

This study introduces an innovative Roughness-CANUPO-Dip-Facet (R-C-D-F) methodology for the measurement of dip angle and direction in geological rock facets. The R-C-D-F method is distinguished by its comprehensive four-step approach, encompassing filtration through roughness analysis, CANUPO analysis, and dip angle filtration, followed by facet segmentation as the measurement step. To achieve precise and efficient results, the method specifically focuses on isolating joint embedment, achieved by systematically filtering out joint bands. This selective filtration process ensures that measurements are conducted exclusively on relevant joint embedment points. The novelty of this methodology lies in its capability to automatically eliminate joint bands while retaining the joint embedment points, facilitating precise measurements without manual intervention. Three site models were evaluated using the R-C-D-F method, alongside four different techniques for measuring dip angle and direction: plane fitting, normal vector conversion, facet segmentation, and compass measurements. The results demonstrated that all methods accurately calculated the dip angle, with an accuracy ranging from 97 % to 99.4 %. The facet segmentation method was selected as the optimal measurement tool due to its automatic nature and capacity to provide accurate results without manual intervention. Furthermore, the optimal local neighbour radius (LNR) for calculating normal vectors was determined, with findings indicating that a larger LNR value enhances accuracy but also increases computational time. A verification was conducted to estimate the dip angle used for filtering and discarding additional points representing joint rock bands, with the optimal value being 45, 30, and 45 degrees for the respective sites.The R-C-D-F method effectively detected and eliminated 100 % of joint band points while retaining 81 % of joint embedment points, and the facet segmentation method provided accurate dip angle and direction measurements for each joint embedment segment. These outcomes underscore the robustness and precision of the R-C-D-F method in geological engineering and rock stability studies.