1,720,963 research outputs found
Orientation of ambiguous image sequences with similar and repeated structures
Get PDFImage orientation, nowadays called Structure from Motion (SfM), is still an open research topic in particular in case of scenes featuring visual aliasing, or doppelgangers. Indeed, visually similar but distinct elements of the scene can cause incorrect matches, not detected by geometric or learning-based outliers removal methods, leading to misplaced camera poses and wrong 3D reconstructions. The paper reviews various state-of-the-art approaches to orient ambiguous image sequences and determination correct camera orientation parameters. We also present an in-house graph-based approach to reliably and precisely orient sets of images with doppelgangers. Different experiments on common ambiguous datasets are reported and commented
Night and Day Aerial Photogrammetry
No full textRecent advancements in aerial imaging, including high-resolution sensors and integrated GNSS/IMU systems, have significantly enhanced photogrammetric methods for geospatial data acquisition. While most aerial data is captured during daylight, night-time imaging is increasingly being used in applications such as urban analysis and disaster assessment. However, automatic co-registration of day and night imagery remains challenging due to substantial radiometric differences. This study investigates the use of deep learning-based feature matching techniques for the alignment of multi-temporal, day-night aerial datasets. Experimental results show that feature extraction is highly sensitive to scale, with only a limited subset of deep learning (DL) methods—particularly ALIKED with LightGlue and SuperPoint with SuperGlue—proving robust under low-illumination conditions. Additionally, a U-Net-like model was trained to pre-process night-time images by approximating their radiometric characteristics to those of daytime images, enabling consistent feature matching across all tested methods. Among them, ALIKED with LightGlue offered the best balance between match quantity and computational efficiency. Object-space evaluations confirmed that the proposed pre-processing step significantly improves co-registration accuracy. The methodology offers a promising foundation for future multi-sensor and multi-modal image alignment tasks, including RGB-thermal and 2D-3D matching
Analyzing Target-, Handcrafted- and Learning-Based Methods for Automated 3D Measurement and Modelling
Get PDFIn industrial vision metrology, precise spatial measurement is vital for quality control and complex manufacturing, traditionally relying on target arrays for sub-pixel accuracy (0.05–0.1 pixels) and precision to beyond 1:200,000. However, target design, placement and measurement are often time-consuming and challenging for large-scale projects. Automated, markerless methods, generally called Structure-from-Motion (SfM), based on handcrafted algorithms or deep learning-based pipelines, offer greater flexibility but are not widely adopted due not only to concerns about reliability and precision, but also because in many industrial photogrammetry applications targets highlight particular feature points of interest, e.g. tooling points, holes and edges. This study reviews the differences between target-, handcrafted- and learning-based approaches, explores hybrid methods combining targets and natural features, and tests learning-based or handcrafted approaches against the traditional target-based method. Two end-to-end learning-based pipelines based on SuperPoint+LightGlue and KeyNet+AffNet+HardNet are evaluated. Results show that deep learning pipelines for tie point extraction provide enhanced automation but inferior triangulation precision, while being comparable to handcrafted methods
LVG-SfM: Learning-Based View-Graph Generation for Robust on-the-Fly SfM
No full textStructure from Motion (SfM) has been widely studied in many fields, such as computer vision, photogrammetry, robotics, etc. Recent advancements focus on improving the real-time performance of SfM, which is crucial for applications in augmented reality, mixed reality, robotics, etc. However, the robustness of real-time processing is still limited by outliers in the feature extraction and matching process, stemming from challenging scenes depicting objects with poor texture, repetitive structures, and symmetric objects, which can cause blunders in the view-graph. Focusing on these scenes, a Learning-based View-Graph generation method (LVG-SfM) is investigated and integrated into the on-the-fly SfM pipeline [43]. First, to provide a higher number of reliable matches and generate a more robust view-graph, a set of SoTA learning-based feature extraction and matching methods [19] are tested. Then, the spuriously incorrect two-view geometries generated from repetitive structures are removed from the view-graph with the help of SoTA learning-based disambiguation network - Doppelgangers [3]. Experimental results demonstrate that our LVG-SfM can successfully work on-the-fly on challenging ambiguous scenes with poor textures and repetitive structures, achieving correct scene reconstructions and robustifying SfM. Project website at: https://sygant.github.io/lvgsfm
TRACENET - A VR Framework to Support Online Collaborative Training Activities
No full textThe article introduces the YR framework and the preliminary outcomes of the ongoing European TRACENET project, an interdisciplinary and collabora tive initiative to support and enhance Civil Protection training activities through 3D geospatial data and YR solutions. The YR framework is a virtua\ and col laborative multi-player environment for manifold and diverse hazard simulations which allows remote users to see the outcomes of their intervention decisions. The YR framework is a complementary solution to traditional training activities, enabling cost reduction of full-scale on-site exercises - included in so-called EU MODEX activities. The framework is based on the Unreal rendering engine and is primarily dedicated to situation assessment, intervention planning and decision making
Geometric Calibration of Thermal Infrared Cameras: A Comparative Analysis for Photogrammetric Data Fusion
Get PDFThe determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work
Separate and Integrated Data Processing for the 3D Reconstruction of a Complex Architecture
Get PDFIn the last few years, data fusion has been an active research topic for the expected advantages of exploiting and combining different but complementary techniques for 3D documentation. The data fusion process consists of merging data coming from different sensors and platforms, intrinsically different, to produce complete, coherent, and precise 3D reconstructions. Although extensive research has been dedicated to this task, we still have many gaps in the integration process, and the quality of the results is hardly sufficient in several cases. This is especially evident when the integration occurs in a later stage, e.g., merging the results of separate data processing. New opportunities are emerging, with the possibility offered by some proprietary tools to jointly process heterogeneous data, particularly image and range-based data. The article investigates the benefits of data integration at different processing levels: raw, middle, and high levels. The experiments are targeted to explore, in particular, the results of the integration on large and complex architectures
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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