Point Cloud Model Research Articles

Real-Time Environmental Contour Construction Using 3D LiDAR and Image Recognition with Object Removal

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Post‐earthquake structural damage identification and safety evaluation using point clouds

AbstractEfficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

UAV-BIM-BEM: An automatic unmanned aerial vehicles-based building energy model generation platform

Many existing and aging buildings were in urgent need of upgrades and renovations, but lack accurate geometric and energy consumption information. This study introduced an unmanned aerial vehicles-based (UAV-based) automated platform for building information modellings (BIM) to generate builidng energy modellings (BEM), which was called the UAV-BIM-BEM platform. The platform enabled the creation of energy models for existing buildings to estimate energy consumption and perform retrofit analysis. A four-story office building in Hong Kong, with dimensions of 52.3 m in length, 14.3 m in width, and 16.5 m in height, was selected as the case study building. Firstly, using UAV oblique photography techniques and path planning algorithms, the building images were transformed into a detailed 3D point cloud model. The point cloud model was provided to Revit to generate the BIM model in IFC format. The IFC file containing the geometric information of the building was then supplied to the AutoBPS-BIM tool, resulting in the generation of the BEM model in EnergyPlus to simulate detailed end-use energy consumption. The annual electricity use intensity (EUI) of the building was estimated to be 145.05 kWh/m2. Lastly, the generated energy model was used to evaluate the energy-saving potential of three different measures. The results showed that the electrical energy savings from cooling equipment, lighting systems, and window replacement are 17.22 %, 6.91 %, and 6.61 %, respectively. This research demonstrated that the UAV-BIM-BEM platform has great potential in BEM modeling for existing buildings and building energy retrofitting.

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule.

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.
Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.
Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.
Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Calibration of multi-robot coordinates for collaborative wire arc additive manufacturing using cross-source 3D point cloud models

To increase the deposition deficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.

Street tree segmentation method combining image semantic segmentation and point cloud clustering

Abstract As a common object in the park scene, the 3D information of street trees is crucial for digital cities, and extracting individual trees from point cloud data accurately and efficiently is a hotspot and a difficult point-in-point cloud processing. Aiming at the interference problem of complex non-tree objects and dynamic objects in the park environment, a tree point cloud segmentation method combining a deep learning network framework with a point cloud clustering algorithm is proposed in this paper. First, this paper applies the YOLOv8 to the semantic segmentation of camera images, and fuses image semantics with point cloud data, to build the semantic point cloud model. Then, preliminary extraction for street trees is performed using the semantic information of this point cloud model, and radius filtering is used to reduce the outliers. Finally, the individual tree segmentation is realized using an enhanced multi-level Euclidean clustering algorithm based on KD-Tree. Experimental results indicate that the proposed method performs well in extracting and segmenting the roadway trees, with a recall of 94.5%, a precision of 80.7%, and an F1-score of 87.0%.

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.

PCM-BESO: a reformative multi-constraints topology optimization method using point cloud modeling oriented to additive manufacturing

PCM-BESO: a reformative multi-constraints topology optimization method using point cloud modeling oriented to additive manufacturing

Accurate and efficient dimensional measurement technique based on point cloud models and its application in the automotive industry

Accurate and efficient dimensional measurement technique based on point cloud models and its application in the automotive industry

Autonomous design framework for deploying building integrated photovoltaics

The advancements in perovskite solar cells present promising prospects for the widespread deployment of Building-Integrated Photovoltaics (BIPVs). Finding an efficient and accurate approach is essential to provide deployment strategies for decision support. This study develops an autonomous decision-making design framework for BIPV, including data collection, 3D modeling, and deployment strategy. For data collection, an open-source unmanned aerial vehicle platform is produced to execute an innovation explore-then-exploit algorithm for viewpoints generation and path planning. Subsequently, point cloud models of buildings are generated using a unique deep learning-based multi-view stereo network and then converted into polygonal surface models. Moreover, a novel Grasshopper plugin component is developed to assess the economic performance of various BIPV layouts by life cycle cost analysis. Based on the analysis results, potential BIPV deployment strategies are provided to support the decision-making process. The effectiveness of the framework is validated through its application in an industrial building in Hong Kong, demonstrating a 7.6 % discrepancy in the average annual solar radiation access value. Finally, two BIPV deployment strategies are proposed for the building. This study has significant implications for the design and deployment of PV systems in urban environments, representing an important step towards supporting the transition to sustainable and low-carbon cities.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

Research and application of 3D spatial safety electronic fence technology based on beidou high-precision positioning in substation construction projects

Abstract In recent years, the Beidou Navigation Satellite System (BDS) has developed rapidly, achieving global coverage and being widely used in various fields. The high-precision positioning capability of the Beidou system provides strong technical support for a variety of application scenarios, especially in fields requiring high-accuracy location information, such as safety monitoring in substation engineering. Substations are critical components of the power system, and their operational safety is vital for the stable operation of the entire grid. However, due to the complexity of the equipment and harsh working environments in substation engineering, utilizing Beidou’s high-precision positioning capabilities can significantly enhance the safety management level of substation construction projects and reduce the occurrence of accidents. This paper primarily explores and investigates high-precision positioning-based three-dimensional (3D) spatial safety electronic fence technology for substation construction projects. The process begins with using laser radar to capture 3D scene information and extract key parts of the point cloud model. Comprehensive positioning sensors, integrating the Beidou Navigation Satellite System (BDS) and inertial navigation, are then installed on crane booms to achieve real-time monitoring of the boom’s posture. Finally, a software platform is established to calculate the real-time 3D spatial distance between the crane boom and surrounding sensitive facilities, ensuring the safety of large-scale construction machinery. Field test results validate the effectiveness of the proposed method.

Automatic modeling of the current state components of ancient buildings based on 3D deformation algorithm

AbstractAncient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model.

A status digital twin approach for physically monitoring over-and-under excavation in large tunnels

Accurate assessments of over-and-under-excavation volumes during tunnel construction are critical for stability and structural safety. Over-and-under-excavation assessment is typically time-consuming as it is undertaken manually and is prone to inaccuracies that adversely impact quality, safety, and costs. To overcome this problem, a status digital twin (to monitor physical conditions) is developed to assess over-and-under excavation during tunneling, enabling a robust and automated assessment of volumes of earth to be determined. The development of the status digital twin comprises three stages: (1) creating an as-designed building information model and as-built point cloud model; (2) producing an automated status update with newly added data; and (3) calculating the of over-and-under excavation volume by building a Triangulated Irregular Network (TIN). The status digital twin is tested in a large-scale tunnel project to evaluate its feasibility and effectiveness. The average over-and-under excavation thickness is 0.05 m, and the mean error of volume calculation is 4.54 %. The results demonstrate that the status digital twin can accurately assess over-and-under excavation during the construction of large-scale tunnel projects by providing instant feedback concerning over/under excavation, thus enabling quality to be effectively managed and costs to be controlled.

Vegetal/digital: Photogrammetry point-clouds of Australian flowers

Arising out of the heightened sensory perceptions of extended lockdown, this creative investigation began with contemplation of flowering street-trees. Through 262 days of lock down, residents of Melbourne retreated to the hyper-local, often reinforced by a 5-km travel bubble and a one-hour daily time limit outdoors. The sublime ephemeral springtime flowers of street-trees were amplified by the extreme sensory and social constraints of social distancing. Drawing us into a suspended moment of slow encounter, we attuned to the contained glowing pulse of plants. The works were created using photogrammetry, a technique for generating 3D models from a large set of photographs taken from all angles of the botanical specimen. Whilst the point-cloud models remain the central artefact of vegetal/digital, it has been presented in a range of formats throughout 2022–23, including multichannel video loops, augmented reality, webXR, gestural interactive works and still prints. This work invites an embodied experience of attuning to both the vegetal and the digital. This is consistent with my alignment with queer and autistic politics, of siding with the object. Increasingly, queer cultural practices are employing new materialism and posthumanism to interrogate the agency of things and the culturally constituted hierarchies of objects and subjects. The reconfiguration of embodied relations is necessary in order to reconceive a viable future.

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.

LiDAR point cloud simplification strategy utilizing probabilistic membership

With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.

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.

Two-stage terrestrial laser scan planning framework for geometric measurement of civil infrastructures

Terrestrial laser scanning plays a crucial role in measuring geometry shapes of civil infrastructures. However, its practical application often encounters challenges such as insufficient model quality and acquisition efficiency. To address these issues, this study proposes a two-stage planning scheme that optimises both scan positioning and single-station scan parameters. Recognising the need to consider both structures and background objects, UAV scanning is employed to capture a three-dimensional point cloud model of the actual scenario. A visibility analysis method based on spherical projection is proposed to enhance computational efficiency of virtual scan simulation. Additionally, an improved particle swarm algorithm is utilised to optimise the scan positioning. During onsite deployment, the scanner’s pose is measured to align structural components with the scanner’s coordinate system, allowing for automated scanning based on occupied angular ranges. The effectiveness of this method is demonstrated through geometry measurements of a small outdoor structure and a suspension bridge.

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.