The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

In the last decade, RGB-D cameras - also called range imaging cameras - have known a permanent evolution. Because of their limited cost and their ability to measure distances at a high frame rate, such sensors are especially appreciated for applications in robotics or computer vision. The Kinect v1 (Microsoft) release in November 2010 promoted the use of RGB-D cameras, so that a second version of the sensor arrived on the market in July 2014. Since it is possible to obtain point clouds of an observed scene with a high frequency, one could imagine applying this type of sensors to answer to the need for 3D acquisition. However, due to the technology involved, some questions have to be considered such as, for example, the suitability and accuracy of RGB-D cameras for close range 3D modeling. In that way, the quality of the acquired data represents a major axis. In this paper, the use of a recent Kinect v2 sensor to reconstruct small objects in three dimensions has been investigated. To achieve this goal, a survey of the sensor characteristics as well as a calibration approach are relevant. After an accuracy assessment of the produced models, the benefits and drawbacks of Kinect v2 compared to the first version of the sensor and then to photogrammetry are discussed.

Heritage documentation has benefitted greatly from significant developments in sensor technology during the past two decades. Miniaturisation of sensors is also an important aspect in the development of low cost sensors, always interesting in heritage projects where budgetary constraints are often present. Among these sensors, the solid-state lidar has begun to attract attention, partly due to its integration in Apple Inc.’s latest version of the iPhone and iPad series. We hypothesise that this type of sensor will see a lot of use in the near future; however, the question remains whether they are sufficient for heritage documentation purposes. In this paper, results from the 2020 iPad Pro SSL point cloud will be assessed and compared to more traditional techniques for 3D scanning (photogrammetry and terrestrial laser scanning). While understandably the geometric quality of benchmark-level techniques such as these remain undeniably better, at least for the moment, the paper concludes that SSL sensors may nevertheless be sufficient for some lower-precision applications.

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.

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.