Mechanical, electrical, and plumbing (MEP) systems play a crucial role in providing various services and creating comfortable environments for urban residents. In order to enhance the management ...efficiency of these highly complex MEP systems, as-built building information models (BIMs) are being increasingly adopted worldwide. As-built BIMs accurately represent the actual conditions of facilities, making as-built BIM reconstruction significantly important for construction progress tracking, quality assurance, subsequent facility management, and renewal. To create as-built BIMs for MEP systems, laser scanners are widely utilized to capture high-resolution images and dense 3D measurements of the environment in a fast and highly accurate manner. Despite research efforts to automatically achieve “Scan-to-BIM,” there are still gaps in applying current solutions to real-world scenarios. One of the major challenges are the limited generalization of existing methods to unseen scenarios without proper training or fine-tuning on custom-designed datasets. To address this issue, this study introduces Omni-Scan2BIM, a novel approach powered by large-scale pre-trained vision foundation models. Omni-Scan2BIM enables the recognition of MEP-related components with a single shot by integrating an all-purpose feature extraction model and a class-agnostic segmentation model. Firstly, given only a single image with a reference mask, the visual features are extracted for both the target component and the collected on-site images using the vision foundation model DINOv2. Secondly, through comparing pixel features, similarity maps are generated for the on-site images. The prior points for the class-agnostic segmentation model are sampled from the local maxima of the similarity map. Thirdly, Segment Anything Model (SAM) is leveraged to sequentially segment the target component. Finally, the target component is segmented out in 3D space based on the transformation matrix describing the spatial relationship between the 2D images and the 3D point clouds. Shape analysis and label fusion are conducted for as-built BIM modeling purpose. To validate the feasibility of the proposed technique, experiments were conducted using data collected from a real construction site in Hong Kong. The results demonstrate that the proposed Omni-Scan2BIM approach can easily generalize to unseen components with significantly improved accuracy and efficiency.
•Ready-to-use Omni-Scan2BIM based on vision foundation models is developed.•One-shot learning-based similarity map and mask generation method is proposed.•2D–3D joint analysis approach to refining segmentation results is presented.•Label fusion strategy integrating multiple classes and setups is developed.•Over 90% of tubular components were successfully recognized on an unseen scenario.
Autonomous navigation in large-scale and complex environments in the absence of a GPS signal is a fundamental challenge encountered in a variety of applications. Since 3-D scans provide inherent ...robustness to ambient illumination changes and the type of the surface texture, we present Point Cloud Map-based Navigation (PCMN), a robust robot navigation system, based exclusively on 3-D point cloud registration between an acquired observation and a stored reference map. It provides a drift-free navigation solution, equipped with a failed registration detection capability. The backbone of the navigation system is a robust point cloud registration method, of the acquired observation to the stored reference map. The proposed registration algorithm follows a hypotheses generation and evaluation paradigm, where multiple statistically independent hypotheses are generated from local neighborhoods of putative matching points. Then, hypotheses are evaluated using a multiple consensus analysis that integrates evaluation of the point cloud feature correlation and a consensus test on the Special Euclidean Group SE(3) based on independent hypothesized estimates. The proposed PCMN is shown to achieve significantly better performance than state-of-the-art methods, both in terms of place recognition recall and localization accuracy, achieving submesh resolution accuracy, both for indoor and outdoor settings.
As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) ...sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.
This letter presents a lossless intra coder of the geometry information of voxelized point clouds. Instead of using the popular octree decomposition, the proposed method views the point cloud ...geometry as an array of bi-level images, and it is inspired by well-known techniques for coding this type of images. This array is encoded using a dyadic decomposition that recursively splits the array into two arrays of half its size, transmitting the occupancy information of each smaller array. Context adaptive arithmetic coding, using both 2D and 3D contexts, is used to achieve efficient compression. Results show that the proposed method outperforms all state-of-the-art intra coders on the public available point cloud datasets tested.
The dynamic development of modern technologies allows the construction of devices that change the current approach to the design and inventory of buildings. The Terrestrial Laser Scanning (TLS) of ...large-scale objects improves the existing state’s documentation and the building reconstruction design phase, especially in the interdisciplinary Architecture, Engineering, and Construction sector(AEC) approach. Understanding these applications’ wide range of technical requirements and considerations is crucial in projects. This research aims to investigate the validity and quality of using TLS for the structural expertise of a metro tunnel. The analysis exploits and focuses on laser scanning technology applications for testing the analysis accuracy of monolithic concrete walls in a building of extended length. The study results indicate that the technological advancement inBuilding Information Modelling (BIM) presents extraordinary opportunities for AEC professionals to use laser scanning in a holistic, collaborative workflow based on 3D model-based design and data quality.
Building instance segmentation is of very importance to parallel reconstruction, management and analysis of building instance. Previous studies of building instance segmentation mainly focused on the ...building scenes where the building spacing is much larger than the point spacing, while the accuracy of building instance segmentation for complex buildings scenes and the building point clouds where the space between buildings is similar with point spacing is low. To improve the accuracy of building instance segmentation for complex building scenes, we propose a novel object-based building instance segmentation (OBBIS) method from airborne light detection and ranging (LiDAR) point clouds. Firstly, our proposed method divides building point clouds into objects, and then the objects are classified according to the characteristics of building roof plane objects, roof accessory objects and building facade objects. Secondly, we use node to represent object and then a fix-size feature vector is inferred for each node. Thirdly, vertical cylinder neighbour node graph is constructed. Finally, the energy function is constructed according to the relationship between the nodes, and then the objects are merged according to the energy minimum (that is, objects are merged with a minimum energy to obtain the building instances). Comprehensive experiments on benchmark datasets demonstrate that the proposed OBBIS method performs better than eight state-of-the-art building instance segmentation methods.
In Scan-to-BIM, semantically understanding 3D point clouds is an essential process that must precede 3D BIM elements generation. The main idea of this research stemmed from an assumption where ...recognizing a group of points would be simpler than assigning label per point from the machine's perspective when using deep learning classifiers. To validate our assumption, conventional point-wise classification problem was formulated as segment-wise classification problem leveraging unsupervised clustering. A single parameter executable hierarchical density-based algorithm and PointNet-based algorithms are chosen for segmentation and classification purposes. Using same baseline architecture, our segment-wise classification framework showed performance boosts of 1.12% and 7.66% for S3DIS and 3DFacilities compared to point-wise classification approach. Solving the nature of class imbalance problem and dataset augmentation ability are determined to be the contributing factors for showing superior results. Given semantic segmentation deep learning algorithm, our proposed framework provides an opportunity to improve the performance leveraging unsupervised method.
•Formulating deep learning-based point-wise classification as segment-wise classification leveraging unsupervised clustering•The proposed segment-wise classification framework showed accuracy increases of 1.12% and 7.66% for S3DIS and 3DFacilities.•Resolving the nature of class imbalance problem by transforming uneven class distribution into a flatter shape•Minimization of the parameter optimization efforts of the model-driven methods for 3D point clouds semantic segmentation
•We present a framework to directly fuse MLS point clouds and TIR image sequences.•A key-points extraction method by line-intersection is presented.•A semi-automatic and an automatic method are ...proposed to find the correspondences.•A non-local mean strategy for data fusion is developed.
Buildings take a large proportion of the total energy consumption in the city area in winter, therefore Thermal Infrared (TIR) images are widely used to evaluate the energy consumption and leakage of the building. To overcome the difficulties of image interpretation and occlusion in TIR images, utilizing thermal information in 3D structures that fuse TIR images with usable georeferenced 3D coordinates of buildings, is mandatory. To accomplish this mission, we propose a strategy to generate a thermal point cloud combining Mobile Laser Scanning (MLS) point clouds and TIR images. At first, a key-points extraction method based on line-intersection is presented, which helps to detect key-points from highly distorted TIR images. Then, the semi-automatic and the automatic correspondence determination algorithm based on restricted RANSAC for 6DOF pose estimation is proposed and tested. Finally, a non-local mean strategy for data fusion is applied, which integrates the thermal attributes from image sequences and the geometric property from the point clouds, resulting in a smooth representation of thermal point clouds with comprehensive radiance property for thermal analysis. The fusion result qualitatively presents thermal radiation of elements of buildings with detailed geometric information. The generated thermal point clouds contribute to evaluating the energy efficiency of buildings, or building blocks, analyzing the invisible building structure or pipelines for retrofit, and monitoring the energy usage of buildings for long-term development.
Robust road boundary extraction and completion play an important role in providing guidance to all road users and supporting high-definition (HD) maps. The significant challenges remain in remarkable ...and accurate road boundary recovery from poor road boundary conditions. This paper presents a novel deep learning framework, named BoundaryNet, to extract and complete road boundaries by using both mobile laser scanning (MLS) point clouds and high-resolution satellite imagery. First, road boundaries are extracted by conducting a curb-based extraction method. Such extracted 3D road boundary lines are used as inputs to feed into a U-shaped network for erroneous boundary denoising. Then, a convolutional neural network (CNN) model is proposed to complete the road boundaries. Next, to achieve more complete and accurate road boundaries, a conditional deep convolutional generative adversarial network (c-DCGAN) with the assistance of road centerlines extracted from satellite images is developed. Finally, according to the completed road boundaries, the inherent road geometries are calculated. The proposed methods were evaluated using satellite imagery and four MLS point cloud datasets with varying densities and road conditions in urban environments. The quality evaluation metrics of 82.88%, 82.43%, 88.86%, and 84.89% were achieved for four data sets. The experimental results indicate that the BoundaryNet model can provide a promising solution for road boundary completion and road geometry estimation.
Efficient three-dimension exploration of a complex indoor environment remains a difficult duration in mapping accuracy requirements for geometric data. Indoor and outdoor mapping, which has been done ...with classical measuring devices for a long time, has come a long way with the use of laser scanning technology in this field. Although terrestrial laser scanning (TLS) point cloud recording provides great convenience in indoor and outdoor mapping, it is disadvantageous in terms of time and cost. Therefore, in our study, we propose a new indoor mapping strategy with wearable mobile laser scanning (WMLS) approach, which uses the simultaneous localization and mapping algorithm, which has been tried recently, as an alternative to TLS point cloud. In this strategy, four test areas with different geometric structures were scanned. A comparative analysis was performed after each test area was scanned with TLS and WMLS devices. For accuracy analysis, reference data were measured with a precision measuring device, and both scan data were compared on a spatial and length basis. The study results showed that the proposed strategy could be an alternative to TLS in terms of accuracy and the time advantage of the WMLS method.
