Documentation of heritage buildings using close-range UAV images: dense matching issues, comparison and case studies

International audienceThree-dimensional (3D) documentation of heritage buildings has long employed both image-based and range-based techniques. Unmanned aerial vehicles (UAVs) provide a particular advantage for image-based techniques in acquiring aerial views, which are difficult to attain using classical terrestrial-based methods. The technological development of optical sensors and dense matching algorithms also complement existing photogrammetric workflows for the documentation of heritage objects. In this paper, fundamental concepts in photogrammetry and 3D reconstruction based on structure from motion (SfM) will be briefly reviewed. Two case studies were performed using two types of UAVs, one being a state-of-the-art platform dedicated to obtaining close-range images. Comparisons with laser scanning data were performed and several issues regarding the aerial triangulation and dense matching results were assessed. The results show that although the dense matching of these UAV images may generate centimetre-level precision, a further increase in precision is often hampered by the quality of the onboard sensor

Flexible Photogrammetric Computations Using Modular Bundle Adjustment: The Chain Rule and the Collinearity Equations

International audienceThe main purpose of this paper is to show that photogrammetric bundle adjustment computations can be sequentially organized into modules. Furthermore, the chain rule can be used to simplify the computation of the analytical Jacobians needed by the adjustment. Novel projection models can be flexibly evaluated by inserting, modifying, or swapping the order of selected modules. As a proof of concept, two variants of the pin-hole projection model with Brown lens distortion were implemented in the open-source Damped Bundle Adjustment Toolbox (DBAT) and applied to simulated and calibration data for a non-conventional lens system. The results show a significant difference for the simulated, error-free, data but not for the real calibration data. The current flexible implementation incurs a performance loss. However, in cases where flexibility is more important, the modular formulation should be a useful tool to investigate novel sensors, data processing techniques, and refractive models

Geospatial recording and point cloud classification of heritage buildings

La documentation du patrimoine bâti a beaucoup évolué ces dernières années grâce au développement de nouveaux capteurs 3D et de nouvelles techniques de relevé 3D. Les données 3D contribuent à la création d'archives fiables et tangibles des sites et des monuments historiques. Vu l'importance des données 3D dans la documentation du patrimoine bâti, le contrôle de qualité est un aspect primordial qui devrait être abordé avant d'entreprendre le traitement du nuage de points. La thèse est ainsi divisée en deux parties. La première partie concerne principalement l'acquisition et le contrôle de qualité des données. Un point important sera l'intégration de la photogrammétrie et de lalaser grammétrie dans le contexte de la documentation d'un site historique à différentes échelles. La deuxième partie de la thèse va aborder le traitement de nuages de points, plus particulièrement la segmentation et la classification de nuages de points. L'aspect multi-échelle de notre approche est importante car dans beaucoup de cas, un bâtiment remarquable se situe dans un quartier historique qui nécessite une segmentation multi-échelle. En combinant ces deux parties, nous avons considéré l'ensemble du processus allant de l'acquisition de données 3D jusqu'à la segmentation et la classification en entités à plusieurs échelles.The documentation of built heritage has seen a significant development these past few decades due to advancements in new 3D sensors and 3D recording techniques. 3D data serve as reliable and tangible archive for historical sites and monuments. Since 3D data have such importance in the field of heritage documentation, quality control is paramount and must be performed before any point cloud processing is even planned to be conducted. The thesis is therefore divided into two parts. The first part concerned mainly the data acquisition and quality control of the point cloud data using the two techniques most commonly used, i.e. photogrammetry and laser scanning. A particular emphasis was also put on the integration of photogrammetry and laser scanning within the context of a multi-scalar documentation of a heritage site. The second part will address the processing of the resulting point cloud, particularly its segmentation and classification. The multi-scalar approach proposed in this thesis is an important point to note, as in many cases a historical building of interest is located in a historical neighbourhood; thus the requirement for a multi-scalar segmentation. By combining these two parts, the thesis had attempted to address the 3D workflow of heritage sites in a holistic manner, from the 3D data acquisition up to the resulting point clouds' segmentation and classification into individual entities in various scale steps

Geospatial recording and point cloud classification of heritage buildings

La documentation du patrimoine bâti a beaucoup évolué ces dernières années grâce au développement de nouveaux capteurs 3D et de nouvelles techniques de relevé 3D. Les données 3D contribuent à la création d'archives fiables et tangibles des sites et des monuments historiques. Vu l'importance des données 3D dans la documentation du patrimoine bâti, le contrôle de qualité est un aspect primordial qui devrait être abordé avant d'entreprendre le traitement du nuage de points. La thèse est ainsi divisée en deux parties. La première partie concerne principalement l'acquisition et le contrôle de qualité des données. Un point important sera l'intégration de la photogrammétrie et de lalaser grammétrie dans le contexte de la documentation d'un site historique à différentes échelles. La deuxième partie de la thèse va aborder le traitement de nuages de points, plus particulièrement la segmentation et la classification de nuages de points. L'aspect multi-échelle de notre approche est importante car dans beaucoup de cas, un bâtiment remarquable se situe dans un quartier historique qui nécessite une segmentation multi-échelle. En combinant ces deux parties, nous avons considéré l'ensemble du processus allant de l'acquisition de données 3D jusqu'à la segmentation et la classification en entités à plusieurs échelles.The documentation of built heritage has seen a significant development these past few decades due to advancements in new 3D sensors and 3D recording techniques. 3D data serve as reliable and tangible archive for historical sites and monuments. Since 3D data have such importance in the field of heritage documentation, quality control is paramount and must be performed before any point cloud processing is even planned to be conducted. The thesis is therefore divided into two parts. The first part concerned mainly the data acquisition and quality control of the point cloud data using the two techniques most commonly used, i.e. photogrammetry and laser scanning. A particular emphasis was also put on the integration of photogrammetry and laser scanning within the context of a multi-scalar documentation of a heritage site. The second part will address the processing of the resulting point cloud, particularly its segmentation and classification. The multi-scalar approach proposed in this thesis is an important point to note, as in many cases a historical building of interest is located in a historical neighbourhood; thus the requirement for a multi-scalar segmentation. By combining these two parts, the thesis had attempted to address the 3D workflow of heritage sites in a holistic manner, from the 3D data acquisition up to the resulting point clouds' segmentation and classification into individual entities in various scale steps

Relevé 3D et classification de nuages de points du patrimoine bâti

The documentation of built heritage has seen a significant development these past few decades due to advancements in new 3D sensors and 3D recording techniques. 3D data serve as reliable and tangible archive for historical sites and monuments. Since 3D data have such importance in the field of heritage documentation, quality control is paramount and must be performed before any point cloud processing is even planned to be conducted. The thesis is therefore divided into two parts. The first part concerned mainly the data acquisition and quality control of the point cloud data using the two techniques most commonly used, i.e. photogrammetry and laser scanning. A particular emphasis was also put on the integration of photogrammetry and laser scanning within the context of a multi-scalar documentation of a heritage site. The second part will address the processing of the resulting point cloud, particularly its segmentation and classification. The multi-scalar approach proposed in this thesis is an important point to note, as in many cases a historical building of interest is located in a historical neighbourhood; thus the requirement for a multi-scalar segmentation. By combining these two parts, the thesis had attempted to address the 3D workflow of heritage sites in a holistic manner, from the 3D data acquisition up to the resulting point clouds' segmentation and classification into individual entities in various scale steps.La documentation du patrimoine bâti a beaucoup évolué ces dernières années grâce au développement de nouveaux capteurs 3D et de nouvelles techniques de relevé 3D. Les données 3D contribuent à la création d'archives fiables et tangibles des sites et des monuments historiques. Vu l'importance des données 3D dans la documentation du patrimoine bâti, le contrôle de qualité est un aspect primordial qui devrait être abordé avant d'entreprendre le traitement du nuage de points. La thèse est ainsi divisée en deux parties. La première partie concerne principalement l'acquisition et le contrôle de qualité des données. Un point important sera l'intégration de la photogrammétrie et de lalaser grammétrie dans le contexte de la documentation d'un site historique à différentes échelles. La deuxième partie de la thèse va aborder le traitement de nuages de points, plus particulièrement la segmentation et la classification de nuages de points. L'aspect multi-échelle de notre approche est importante car dans beaucoup de cas, un bâtiment remarquable se situe dans un quartier historique qui nécessite une segmentation multi-échelle. En combinant ces deux parties, nous avons considéré l'ensemble du processus allant de l'acquisition de données 3D jusqu'à la segmentation et la classification en entités à plusieurs échelles

Automatic point cloud noise masking in close range photogrammetry for buildings using ai-based semantic labelling

The use of AI in semantic segmentation has grown significantly in recent years, aided by developments in computing power and the availability of annotated images for training data. However, in the context of close-range photogrammetry, although working with 2D images, AI is still used mostly for 3D point cloud segmentation purposes. In this paper, we propose a simple method to apply such methods in close range photogrammetry by benefitting from deep learning-based semantic segmentation. Specifically, AI was used to detect unwanted objects in a scene involving the 3D reconstruction of a historical building façade. For these purposes, classes e.g., sky, trees, and electricity poles were considered as noise. Masks were then created from the results which would then constraint the dense image matching process to only the wanted classes. In this regard, the resulting dense point cloud essentially projected the 2D semantic labels into the 3D space, thus excluding noise and unwanted object classes from the 3D scene. Our results were compared to manual image masking and managed to achieve comparable results while requiring only a fraction of the processing time when using a pre-trained DL network to do the task.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

Teknik Pencocokan Citra dalam Fotogrametri untuk Dokumentasi Cagar Budaya

International audienceToday the image matching technique is used extensively in photogrammetry. This method, which originated from the computer vision domain, facilitates several tasks within the photogrammetric workflow which previously had to be performed manually. Photogrammetry itself is a technique which is often employed in the documentation of heritage as well as archaeology in order to create 3D models. Photogrammetry can be an alternative or a complement to laser scanning in producing accurate and reality-based 3D models. This paper will summarize the state of the art of the image matching technique often used in modern photogrammetry software packages. Several case studies of its use in the area of heritage documentation will also be presented

Evaluation of Azure Kinect Derived Point Clouds to Determine the Presence of Microhabitats on Single Trees Based on the Swiss Standard Parameters

In the last few years, a number of low-cost 3D scanning sensors have been developed to reconstruct the real-world environment. These sensors were primarily designed for indoor use, making them highly unpredictable in terms of their performance and accuracy when used outdoors. The Azure Kinect belongs to this category of low-cost 3D scanners and has been successfully employed in outdoor applications. In addition, this sensor possesses features such as portability and live visualization during data acquisition that makes it extremely interesting in the field of forestry. In the context of forest inventory, these advantages would allow to facilitate the task of tree parameters acquisition in an efficient manner. In this paper, a protocol was established for the acquisition of 3D data in forests using the Azure Kinect. A comparison of the resulting point cloud was performed against photogrammetry. Results demonstrated that the Azure Kinect point cloud was of suitable quality for extracting tree parameters such as diameter at breast height (DBH, with a standard deviation of 2.2cm). Furthermore, the quality of the visual and geometric information of the point cloud was evaluated in terms of its feasibility to identify microhabitats. Microhabitats represent valuable information on forest biodiversity and are included in Swiss forest inventory measurements. In total, five different microhabitats were identified in the Azure Kinect Point cloud. The measurements were therefore comparable to sensors such as terrestrial laser scanning and photogrammetry. Therefore, we argue that the Azure Kinect point cloud can efficiently identify certain types of microhabitats and this study presents a first approach of its application in forest inventories.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

COMPARISON AND ASSESSMENT OF 3D REGISTRATION AND GEOREFERENCING APPROACHES OF POINT CLOUDS IN THE CASE OF EXTERIOR AND INTERIOR HERITAGE BUILDING RECORDING

International audienceIn the field of 3D heritage documentation, point cloud registration is a relatively common issue. With rising needs for Historic Building Information Models (HBIMs), this issue has become more important as it determines the quality of the data to be used for HBIM modelling. Furthermore, in the context of historical buildings, it is often interesting to document both the exterior façades as well as the interior. This paper will discuss two approaches of the registration and georeferencing of building exterior and interior point clouds coming from different sensors, namely the independent georeferencing method and the free-network registration and georeferencing. Building openings (mainly windows) were used to establish common points between the systems. These two methods will be compared in terms of geometrical quality, while technical problems in performing them will also be discussed. Furthermore, an attempt to automate some parts of the workflow using automatic 3D keypoints and features detection and matching will also be described in the paper. Results show that while both approaches give similar results, the independent approach requires less work to perform. However, the free-network method has the advantage of being able to compensate for any systematic georeferencing error on either system. As regards to the automation attempt, the use of 3D keypoints and features may reduce processing time; however correct tie point correspondence filtering remains difficult in the presence of heavy point cloud noise