CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.</p

Combining image and point cloud segmentation to improve heritage understanding

Current 2D and 3D semantic segmentation frameworks are developed and trained on specific benchmark datasets, often rich of synthetic data, and when they are applied to complex and real-world heritage scenarios they offer much lower accuracy than expected. In this work, we present and demonstrate an early and late fusion of methods for semantic segmentation in cultural heritage applications. We rely on image datasets, point clouds and BIM models. The early fusion utilizes multi-view rendering to generate RGBD imagery of the scene. In contrast, the late fusion approach merges image-based segmentation with a Point Transformer applied to point clouds. Two scenarios are considered and inference results show that predictions are primarily influenced by whether the scene has a predominantly geometric or texture-based signature, underscoring the necessity of fusion methods

Combining image and point cloud segmentation to improve heritage understanding

Current 2D and 3D semantic segmentation frameworks are developed and trained on specific benchmark datasets, often rich of synthetic data, and when they are applied to complex and real-world heritage scenarios they offer much lower accuracy than expected. In this work, we present and demonstrate an early and late fusion of methods for semantic segmentation in cultural heritage applications. We rely on image datasets, point clouds and BIM models. The early fusion utilizes multi-view rendering to generate RGBD imagery of the scene. In contrast, the late fusion approach merges image-based segmentation with a Point Transformer applied to point clouds. Two scenarios are considered and inference results show that predictions are primarily influenced by whether the scene has a predominantly geometric or texture-based signature, underscoring the necessity of fusion methods

IMAGE RECORDING CHALLENGES FOR PHOTOGRAMMETRIC CONSTRUCTION SITE MONITORING

Construction site monitoring and progress monitoring is becoming increasingly popular in the architecture, engineering and construction (AEC) industry. To this end remote sensing techniques are used to gather consecutive datasets of the construction site. This work focuses on the recording of imagery for photogrammetric processing and the challenging conditions often encountered on construction sites. The constantly evolving character of a such sites requires datasets to be captured as quickly as possible. Furthermore other recording complexities arise such as the presence of auxiliary equipment and clutter or reflections caused by wet surfaces, hindering quick and complete recordings. Apart from these external factors also construction elements themselves often complicate the capturing workflow.This work enumerates several real-world examples of difficulties construction sites pose for the recording of imagery for photogrammetry purposes. Each section provides an insight in a specific challenge, typical for construction sites, and discusses applicable field-tested solutions including an overview of relevant solutions found in literature.</p

REVIEW OF WINDOW AND DOOR TYPE DETECTION APPROACHES

The use of as-built Building Information Models (BIM) has become increasingly commonplace. This process of creating a BIM model from point cloud data, also referred to as Scan-to-BIM, is a mostly manual task. Due to the large amount of manual work, the entire Scan-to-BIM process is time-consuming and error prone. Current research focuses on the automation of the Scan-to-BIM pipeline by applying state-of-the-art techniques on its consecutive steps including the data acquisition, data processing, data interpretation and modelling. By automating the matching and modelling of window and door objects, a considerable amount of time can be saved in the Scan-to-BIM process. This is so because each window and door instance needs to be examined by the modeller and must be adapted to the actual on-site situation. Large object libraries containing predefined window and door objects exists but the matching to the best-fit predefined object remains time consuming. The aim of this research is to examine the possibilities to speed up the modelling of window and door objects. First, a literature review discussing existing methods for window and door detection and matching is presented. Second, the acquired data is examined to explore the capabilities of capturing window and door information for different remote sensing devices. Followed by tests of some commonplace features in the use for window and door occurrence matching and clustering

NK cell function is markedly impaired in patients with chronic lymphocytic leukaemia but is preserved in patients with small lymphocytic lymphoma

Chronic lymphocytic leukemia (B-CLL) and small lymphocytic lymphoma (SLL) are part of the same disease classification but are defined by differential distribution of tumor cells. B-CLL is characterized by significant immune suppression and dysregulation but this is not typical of patients with SLL. Natural killer cells (NK) are important mediators of immune function but have been poorly studied in patients with B-CLL/SLL. Here we report for the first time the NK cell phenotype and function in patients with B-CLL and SLL alongside their transcriptional profile. We show for the first time impaired B-CLL NK cell function in a xenograft model with reduced activating receptor expression including NKG2D, DNAM-1 and NCRs in-vitro. Importantly, we show these functional differences are associated with transcriptional downregulation of cytotoxic pathway genes, including activating receptors, adhesion molecules, cytotoxic molecules and intracellular signalling molecules, which remain intact in patients with SLL. In conclusion, NK cell function is markedly influenced by the anatomical site of the tumor in patients with B-CLL/SLL and lymphocytosis leads to marked impairment of NK cell activity. These observations have implications for treatment protocols which seek to preserve immune function by limiting the exposure of NK cells to tumor cells within the peripheral circulation

The impact of kombi-taxis on public transport

Includes bibliography.This thesis attempts to quantify the impact of kombi-taxis on the conventional modes of public transport, in particular the bus, in the Cape Town Metropolitan Area. The impact is quantified in terms of the resultant modal shift of commuters from the buses, trains, cars and walking, in favour of the kombi-taxi. The approach adopted involved a study of the kombi-taxi and bus operations and characteristics on the different kombi-taxi routes in the study area. Five representative routes were selected for a detailed study, involving an Observation survey and an Interview survey directed at the bus and kombi-taxi users on these routes. On a further 66 routes, a bus-taxi modal split survey was conducted. The findings of the study show• that the majority of present kombi-taxi users are former bus users. Conservatively, an estimated 30.6% of all the daily bus passenger trips have been lost to the kombi-taxi. The effect on trains has not been insignificant with an estimated 4.4% of all commuter train trips having been converted to kombi-taxi trips

Automated reconstruction of Building Information Model objects from point cloud data

As-built Building Information Models are becoming increasingly popular in the Architectural, Engineering and Construction industry. These models reflect the state of a building up to as-built conditions and are used in numerous applications such as refurbishment, facility management and project planning. The production of these models includes the acquisition of the structure with remote sensing techniques and the manual placement of the target objects. However, the interpretation of the acquired point cloud data is time-consuming and prone to misinterpretations. Aside from the overwhelming data size, modelers struggle with occlusions, noise and clutter in the building environment. This work presents a series of automated procedures to cope with the current problems. More specifically, both theoretical and practical solutions are proposed to aid in the data acquisition, point cloud interpretation and object reconstruction. The following contributions are part of this research. Data Acquisition The purpose of the data acquisition is to produce highly dense accurate point cloud data. An extensive literature study is performed to explore the opportunities of both static and dynamic data acquisition systems for building surveying. It is concluded that while mobile solutions have superior scene coverage and acquisition speed, terrestrial laser scanners currently have the upper hand in producing high-quality point clouds fit for as-built modeling. A practical study proves that the current workflow, which includes control measurements with total stations, can be significantly enhanced by integrating compensator data in the registration of the individual scans. One of the major conclusions is that terrestrial laser scanning can be used as a standalone solution in mid-to-large scale projects without the need for control measurements, while still producing data conform the metric accuracy requirements. Data Interpretation Once the data is aligned in a common coordinate system, the point clouds are interpreted to determine which points belong to the objects of interest. The goal is to make a proper interpretation of the data to increase the level of information. Instead of a manual procedure, a fully automated workflow is proposed which consists of three consecutive steps. First, the data is segmented into planar primitives according to the surface hypothesis of the structural elements in the observed data. Next, each of the observed segments is assigned a predefined semantic class label through a pretrained machine learning algorithm. The resulting classified instances are clustered together using graph theory in order to isolate all the available observations of each relevant object. From the experiments it could be derived that the majority of the observations of structural objects in a building environment can be properly extracted. While some false positives remain a problem, the implementation of a fully 3D approach shows promising results for point cloud interpretation. Data Reconstruction The grouped observations of each object are used to reconstruct a set of generic Building Information Modeling entities conform existing standards. The target is to create a set of parametric BIM objects that are usable by the industry. A class specific reconstruction algorithm is proposed to extract the necessary properties for the parametric representation of the walls. After the creation of a set of partial objects, the topology is also reconstructed based on intersection theory. Overall, the experiments show that despite the abstractions of the class definitions, the automated workflow is capable of reconstructing topologically consistent wall geometry.status: publishe

CASE STUDY FOR UAS-ASSISTED BRIDGE INSPECTIONS

Bridge inspections are typically expensive and time-consuming, especially in regards of the inspection of difficult-to-reach areas. In recent years, unmanned aerial systems (UASs) have gained attention due to their flexible data acquisition. However, UAS inspections generate large quantities of image and video data, which are currently analysed manually. Additionally, identified damages are currently not assessed accurately in their geometric characteristics and location. In this paper, we propose a time-effective framework for a UAS-based bridge inspection methodology that combines 3D information from photogrammetry and machine learning based object detection to allow direct measurements in the images. Concretely, we propose the use of a two-step flight planning to accurately reconstruct the bridge using limited manual effort. Second, we detect frequently occurring damages such as exposed rebars and concrete spalling on the inspection imagery. Finally, we use the spatial location of the imagery to significantly improve the detection results and geolocate them. We evaluate our proposed framework on a decommissioned concrete bridge. The trained YOLOv8 models prove capable of transfer learning on both our own data and online benchmarks. The photogrammetric reconstruction also proves to be sufficiently reliable. Overall, these are the first steps in automating routine bridge inspections and provide crucial evidence to continue developing the method

STANDALONE TERRESTRIAL LASER SCANNING FOR EFFICIENTLY CAPTURING AEC BUILDINGS FOR AS-BUILT BIM

With the increasing popularity of as-built building models for the architectural, engineering and construction (AEC) industry, the demand for highly accurate and dense point cloud data is rising. The current data acquisition methods are labour intensive and time consuming. In order to compete with indoor mobile mapping systems (IMMS), surveyors are now opting to use terrestrial laser scanning as a standalone solution. However, there is uncertainty about the accuracy of this approach. The emphasis of this paper is to determine the scope for which terrestrial laser scanners can be used without additional control. Multiple real life test cases are evaluated in order to identify the boundaries of this technique. Furthermore, this research presents a mathematical prediction model that provides an indication of the data accuracy given the project dimensions. This will enable surveyors to make informed discussions about the employability of terrestrial laser scanning without additional control in mid to large-scale projects