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The majority of buildings are existing and have not been constructed in a BIM process. That is why, the modelling of existing buildings becomes a major issue. Beyond the questions of maintenance or renovation with the promise of reducing the environmental impact, their modelling is of interest for documentation and valorisation. But today, while the acquisition techniques are significantly progressing, with the use of efficient laser scanners, the modelling remains manual and very time consuming. The literature is not empty of proposals to automate the process. Nevertheless, many studies are based on strict architectural hypotheses or restricted to unoccupied buildings free of furniture. This strongly limits their application field. In response to these limitations, this paper presents an innovative method retaining only verticality of walls as assumptions. It is based on occupancy and density image analysis. Tested on a wide variety of buildings, this method is very promising with very few classifications errors. Furthermore, the process is successful with dynamic laser scanning data, in cluttered environments, and applied on buildings with a non-Manhattan-World scheme. [ABSTRACT FROM AUTHOR]

With the popularization of RGB-D cameras, access to the third dimension is now possible at low cost. However, these systems have a lower accuracy compared to other technologies such as terrestrial laser scanners (TLS) or mobile laser scanners (MLS). RGB-D cameras have proved their potential for 3D indoor mapping, particularly for Building Information Models reconstruction (Li et al., 2020). This paper aims to investigate the acquisition protocol and propose a method for BIM reconstruction by using an RGB-D camera (Kinect Azure). First, an acquisition protocol is established with the aim of improving the quality of 3D reconstruction of indoor scenes. Different scene cases are considered, namely a single wall, a corridor, a room (with different levels of detail) and two adjacent rooms. After having extracted the best acquisition scenarios for each case of the studied scenes, an image processing is performed for the most complex scenes. Then the 3D reconstruction is performed and the resulting point clouds are subsampled and cleaned. Next, an evaluation of the geometric quality of the 3D reconstruction is performed, by making a comparison between the point clouds from the acquisition protocol (room and corridor) and the reference point clouds from an MLS. The results of this comparison shows that the differences between the two point clouds have an absolute average deviation that doesn't exceed 4.8mm, which proves that the proposed method has reached competitive accuracy. Finally, segmentation and 3D modeling of the studied scenes are proceeded to extract the BIM objects. [ABSTRACT FROM AUTHOR]

Nowadays, Mobile Laser Scanning (MLS) systems are more and more used to realize extended topographic surveys of roads. Most of them provide for each measured point an attribute corresponding to a return signal strength, so called intensity value. This value enables to easily understand uncolored MLS as it helps to differentiate materials based on their albedo. In a road context, this intensity information allows to distinguish, among others, the main subject of this paper, i.e. road markings. However, this task is challenging. Road marking detection from dense MLS point cloud is widely studied by the research community. It might concern road management and diagnosis, intelligent traffic systems, high-definition maps, location and navigation services. Dense MLS point clouds provided by surveyors are not processed online, they are thus not directly applicable to autonomous driving, but those dense and precise data can be for instance used for the generation of HD reference maps. This paper presents a review of the different processing chains published in the literature. It underlines their contributions and highlights their potential limitations. Finally, a discussion and some suggestions of improvement are given. [ABSTRACT FROM AUTHOR]

The thermal infrared study of urban environments is of growing interest. It allows to observe the variations of surface temperatures on objects over time and therefore the microclimate at the scale of a street. To facilitate the analysis of thermal interactions between urban elements, it is necessary to provide a 3D visualization of the thermography of a street. For this purpose, 3D thermal models combining geometric and thermal infrared (TIR) measurements are required. The chosen format for 3D thermal models is a point cloud with a temperature attribute. In our approach, two types of urban components are considered: buildings and trees. The geometric data of each component are acquired with a static laser scanner and the surface temperature is acquired with a thermal handheld camera. For the building, the approach consists in georeferencing TIR images and colorize the point cloud by projection. For trees, the approach consists of the colorization of each laser scan prior to the registration. The spherical panoramic images acquired with the Terrestrial Laser Scanning (TLS) are used as references to automatically georeferenced the TIR and thus to save time. The 3D thermal models obtained highlight the impact of sunlight on buildings and trees. At building scale, this thermal representation also helps to emphasize thermal bridges, as well as the shadow generated by surrounding trees. At tree scale, this representation is useful for monitoring the temporal and spatial variability of trunk's and leave's temperatures. Obviously, the thermal models underline the impact of trees on the urban environment. [ABSTRACT FROM AUTHOR]

The simulation of the energy balance at the scale of a street become a huge issue. The accurate geometry of the area, detailed atmospheric measurements, and eco-physiological information are necessary to perform faithful simulations. In addition, thermal images showing surface temperatures are also of highly interest to analyse the energy balance between façades and trees. In this context, the aim of our project is to contribute to the development of a methodology for merging 3D geometry of an urban scene with surface temperatures. In this paper, a methodology is proposed for the texturing of a point cloud from a static laser scanner with IR images from a thermal camera. The main idea of the developed methodology is to take part of the ability of the thermal camera to acquire simultaneously RGB and IR images. After determining the relative position and orientation of IR and RGB sensors, the orientations of IR images are deduced from the orientations of RGB images. Then, the point cloud and RGB images are registered by selecting homologous points manually. Finally, the point cloud is textured with the IR images. [ABSTRACT FROM AUTHOR]

Dense point clouds acquired with a mobile laser scanning system (MLS) device become usual raw data for different surveyor applications: topographic maps, 3D models, road inventories, risk assessment of vegetation on road or railroads. Thanks to important evolutions in technologies, MLS devices became powerful and very popular. In the meantime, the need for point cloud automatic processing tools is growing. However, the available tools have not yet reached a sufficient level of maturity. Using MLS point clouds to produce topographic maps, BIM model or other deliverables, requires very often manual vectorization (or digitalization) work. In the road context, the transition from point cloud to road map that consists in delineating curb or road edges, road markings, pole, trees, facades etc. is currently performed manually. To reduce these time-consuming operations, several solutions have been proposed in the literature. In this paper we present the first results of a method consisting in vectorizing urban point cloud scene. The originality of this work is to propose a global approach aiming to detect and vectorize simultaneously multiple objects. The developed algorithm uses cross-section analysis to detect road curbs and vertical objects. The first results are promising, since an F-score higher than 80% has been reached, even before applying road logic rules or additional knowledge. The detection and extraction of vertical objects including facades, trees, and poles, is more challenging but the detections also present a recall greater than 85%. [ABSTRACT FROM AUTHOR]

While BIM (Building Information Modelling) appears as a solution to reduce the cost and environmental impact of buildings, its implementation on existing buildings is still a major challenge. In the last five years, an important number of publications on the topic have been published. This paper proposes an up-to-date overview about the automation of as-built BIM creation from point clouds, focused on indoor environment. It is structured in two main parts. The first one deals with the segmentation and classification of point clouds by storey, rooms, walls, and slabs. The second one focuses on the modelling, strictly speaking, of the main elements of an indoor scene. The approaches are grouped into principal ideas. Through the presentation of methods using new types of scanners and associated sensors, it highlights the promising use of other information in addition to 3D geometry. [ABSTRACT FROM AUTHOR]

With their high recording rate of hundreds of thousands of points acquired per second, speed of execution and a remote acquisition mode, SLAM based mobile mapping systems (MMS) are a very powerful solution for capturing 3D point clouds in real time, simply by walking in the area of interest. Regarding indoor surveys, these MMS have been integrated in handheld or backpack solutions and become fast scanning sensors. Despite their advantages, the geometric accuracy of 3D point clouds guaranteed with these sensors is lower than the one reachable with static TLS. In this paper the effectiveness of two recent mobile mapping systems namely the GeoSLAM ZEB-REVO RT and the more recent GreenValley LiBackPack C50 is investigated for indoor surveys. In order to perform a reliable assessment study, several datasets produced with each sensor are compared to the high-cost georeferenced point cloud obtained with static laser scanning target-based technique. [ABSTRACT FROM AUTHOR]

In the context of building renovation, energy audits of buildings are performed in order to reduce energy consumption and greenhouse gas emissions. Infrared cameras allow analyzing precisely the energetic performances and the thermal comfort of a building and the analysis of thermal images of buildings represents a key step on the way to the reduction of energy consumption. In the last few years, the combination of thermal and metric information has been increasingly studied. The geometry provides the exact location of defects and enables accurate energy analysis. This paper focuses on the automatic analysis of facades point clouds with thermal attributes. The aim of our work is to extract windows and thermal bridges by considering both geometric and thermal features. The extraction of objects or zones of interest in thermal data consists in the detection of significant temperature variations. The developed approach, mainly based on the automatic analysis of histograms, is applied to a point cloud acquired by a Mobile Mapping Unit. [ABSTRACT FROM AUTHOR]

The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated database. [ABSTRACT FROM AUTHOR]

In the context of architectural heritage preservation, constructing building information models is an important task. However, conceiving a pertinent model is a difficult, time consuming and user-dependent task. Our laboratory has been researching methods to decrease the time and errors inferred by manual segmentation of point clouds. In the perspective of automatization of the process, we implemented an automated registration method that used only keypoints. Keypoints are special points that hold more information about the global structure of the cloud. In order to detect keypoints, we used the Point Cloud Library (PCL) toolbox. The pertinence of the method was evaluated by registering more than 300 clouds of the zoological museum of Strasbourg. The quality of the keypoint detection was first verified on geo-referenced indoor point clouds. Then we applied this method to register the indoor and outdoor point clouds that have much less area in common; those common points being generally the doors and windows of the façade. The registrations of indoor point clouds were satisfying, with mean distances to the ground truth inferior to 20 cm. While the first result for joint indoor/outdoor registration are promising, it may be improved in future works. [ABSTRACT FROM AUTHOR]

In the context of building renovation, infrared (IR) cameras are widely used to perform the energy audit of buildings. They allow analysing precisely the energetic performances of existing buildings and thermal analyses represent a key step for the reduction of energy consumption. They are also used to assess the thermal comfort of people living or working in a building. Building Information Models (BIM) are widespread to plan the rehabilitation of existing buildings and laser scanning is now commonly used to capture the geometry of buildings for as-built BIM creation. The combination of thermographic and geometric data presents a high number and variety of applications (Lagüela and Díaz-Vilariño, 2016). However, geometric and thermal information are generally acquired separately by different building stakeholders and thermal analyses are performed with independence of geometry. In this paper, the combination of thermal and geometric information is investigated for indoor of buildings. The aim of the project is to create 3D thermographic point clouds based on data acquired by a laser scanner and a thermal camera. Based on these point clouds, BIM models might be enriched with thermal information through the scan-to-BIM process. [ABSTRACT FROM AUTHOR]

As the use of building information model (BIM) for architectural heritage becomes more relevant, this paper explores different solutions to further automatize the modelling process. The scan-to-BIM process still requires manual intervention that is time consuming, subject to errors and user-dependent. In this paper, the main focus is the automated segmentation of windows. In the first part of our paper, we will review and compare several state-of-the-art methods for automatic detection and segmentation of openings in a point cloud. Based on the most pertinent aspects of those methods, a new algorithm focusing on indoor point clouds is proposed. After walls are already detected, they are converted in 2D binary images. Holes in those images correspond to openings. We submit each opening to an energy function with two terms: data and coherence. The data term depends on the shape of the opening. The coherence term considers the position of the opening in the scene. Those function let us determine if an opening in the point cloud is due to a window/door or an object obstructing the acquisition. In the third part we discuss the results obtained by applying the method to different datasets. [ABSTRACT FROM AUTHOR]

The French collection of Plan-Reliefs, scale models of fortified towns, constitutes a precious testimony of the history of France. The aim of the URBANIA project is the valorisation and the diffusion of this Heritage through the creation of virtual models. The town scale model of Strasbourg at 1/600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. In this paper, the photogrammetric recording of this scale model is first presented. The acquisition protocol as well as the data postprocessing are detailed. Then, the modelling of the city and more specially building blocks is investigated. Based on point clouds of the scale model, the extraction of roof elements is considered. It deals first with the segmentation of the point cloud into building blocks. Then, for each block, points belonging to roofs are identified and the extraction of chimney point clouds as well as roof ridges and roof planes is performed. Finally, the 3D parametric modelling of the building blocks is studied by considering roof polygons and polylines describing chimneys as input. In a future works section, the semantically enrichment and the potential usage scenarios of the scale model are envisaged. [ABSTRACT FROM AUTHOR]

In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented. [ABSTRACT FROM AUTHOR]

RGB-D cameras, also known as range imaging cameras, are a recent generation of sensors. As they are suitable for measuring distances to objects at high frame rate, such sensors are increasingly used for 3D acquisitions, and more generally for applications in robotics or computer vision. This kind of sensors became popular especially since the Kinect v1 (Microsoft) arrived on the market in November 2010. In July 2014, Windows has released a new sensor, the Kinect for Windows v2 sensor, based on another technology as its first device. However, due to its initial development for video games, the quality assessment of this new device for 3D modelling represents a major investigation axis. In this paper first experiences with Kinect v2 sensor are related, and the ability of close range 3D modelling is investigated. For this purpose, error sources on output data as well as a calibration approach are presented. [ABSTRACT FROM AUTHOR]