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Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development. [ABSTRACT FROM AUTHOR]

In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved. [ABSTRACT FROM AUTHOR]

In this paper, a novel framework of the 3D reconstruction of buildings is proposed, focusing on remote sensing super-generalized stereo-pairs (SGSPs). As we all know, 3D reconstruction cannot be well performed using nonstandard stereo pairs, since reliable stereo matching could not be achieved when the image-pairs are collected at a great difference of views, and we always failed to obtain dense 3D points for regions of buildings, and cannot do further 3D shape reconstruction. We defined SGSPs as two or more optical images collected in less constrained views but covering the same buildings. It is even more difficult to reconstruct the 3D shape of a building by SGSPs using traditional frameworks. As a result, a dynamic multi-projection-contour approximating (DMPCA) framework was introduced for SGSP-based 3D reconstruction. The key idea is that we do an optimization to find a group of parameters of a simulated 3D model and use a binary feature-image that minimizes the total differences between projection-contours of the building in the SGSPs and that in the simulated 3D model. Then, the simulated 3D model, defined by the group of parameters, could approximate the actual 3D shape of the building. Certain parameterized 3D basic-unit-models of typical buildings were designed, and a simulated projection system was established to obtain a simulated projection-contour in different views. Moreover, the artificial bee colony algorithm was employed to solve the optimization. With SGSPs collected by the satellite and our unmanned aerial vehicle, the DMPCA framework was verified by a group of experiments, which demonstrated the reliability and advantages of this work. [ABSTRACT FROM AUTHOR]

In order to adapt to the actual scene of a stereo satellite observing the same area sequentially and improve the accuracy of the target-oriented 3D reconstruction, this paper proposed a building DSM fusion update method based on adaptive splines and target characteristic guidance. This method analyzed the target characteristics of surface building targets to explore their intrinsic geometric structure information, established a nonlinear fusion method guided by the target characteristics to achieve the effective fusion of multiple DSMs on the basis of maintaining the target structural characteristics, and supported the online updating of DSM to ensure the needs of practical engineering applications. This paper presented a DSM fusion method for surface building targets and finally conducted DSM fusion experiments using typical urban area images of different scenes. The experimental results showed that the proposed method can effectively constrain and improve the DSM of buildings, and the integrity of the overall construction of the target 3D model structure was significantly improved, indicating that this paper provides an effective and efficient DSM constraint method for buildings. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors' coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors' geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction. The proposed methods have been validated with synthetic and real data. [ABSTRACT FROM AUTHOR]

Currently, three-dimensional (3D) point clouds are widely used in the field of remote sensing and mapping, including the measurement of living vegetation volume (LVV) in cities. However, the existing quantitative methods for LVV measurement are mainly based on single tree modeling or on the calculation of single tree species' growth parameters, which cannot be applied to the many tree species and complex forest layer structures present in urban regions, and thus are unsuitable for broad application. LVV measurement is based primarily on vegetation point cloud data, which can be obtained through many methods and often lack some information, thus posing problems in the use of traditional LVV measurement methods. To address the above problems, this paper proposes a novel LVV estimation model, which combines the voxel measurement method with an organizing point cloud based on an octree structure (we called it VO-LVV), to estimate the LVV of typical vegetation and landforms in cities. The point cloud data of single plants and multiple plants were obtained through preprocessing to verify the improvement in the calculation efficiency and accuracy of the proposed method. The results indicated that the VO-LVV estimation method, compared with the traditional method, enabled substantial efficiency improvement and higher calculation accuracy. Furthermore, the new method can be simultaneously applied to scenarios of single plants and multiple plants, and can be used for the calculation of LVV in areas with various vegetation types in cities. [ABSTRACT FROM AUTHOR]

Urban environments are regions in which spectral variability and spatial variability are extremely high, with a huge range of shapes and sizes, and they also demand high resolution images for applications involving their study. Due to the fact that these environments can grow even more over time, applications related to their monitoring tend to turn to autonomous intelligent systems, which together with remote sensing data could help or even predict daily life situations. The task of mapping cities by autonomous operators was usually carried out by aerial optical images due to its scale and resolution; however new scientific questions have arisen, and this has led research into a new era of highly-detailed data extraction. For many years, using artificial neural models to solve complex problems such as automatic image classification was commonplace, owing much of their popularity to their ability to adapt to complex situations without needing human intervention. In spite of that, their popularity declined in the mid-2000s, mostly due to the complex and time-consuming nature of their methods and workflows. However, newer neural network architectures have brought back the interest in their application for autonomous classifiers, especially for image classification purposes. Convolutional Neural Networks (CNN) have been a trend for pixel-wise image segmentation, showing flexibility when detecting and classifying any kind of object, even in situations where humans failed to perceive differences, such as in city scenarios. In this paper, we aim to explore and experiment with state-of-the-art technologies to semantically label 3D urban models over complex scenarios. To achieve these goals, we split the problem into two main processing lines: first, how to correctly label the façade features in the 2D domain, where a supervised CNN is used to segment ground-based façade images into six feature classes, roof, window, wall, door, balcony and shop; second, a Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) workflow is used to extract the geometry of the façade, wherein the segmented images in the previous stage are then used to label the generated mesh by a "reverse" ray-tracing technique. This paper demonstrates that the proposed methodology is robust in complex scenarios. The façade feature inferences have reached up to 93% accuracy over most of the datasets used. Although it still presents some deficiencies in unknown architectural styles and needs some improvements to be made regarding 3D-labeling, we present a consistent and simple methodology to handle the problem. [ABSTRACT FROM AUTHOR]

In recent years, the application of geomatics tools in archaeology has proved to be very useful to obtain meaningful knowledge of the 3D reconstruction of archaeological remains and semantic classification of the 3D surface. These techniques have proven to be an effective solution for the 3D modeling and the extraction of many spatial features on an archaeological site. However, novel methodologies as well as new data exploitation strategies are required to exploit these geospatial data for natural and cultural heritage documentation, monitoring, and preservation. In this paper, we have studied unique archaeological ruins, a Mozarab church in Al-Andalus, using high-resolution RGB images, which was taken by a drone. Thus, a 3D reconstruction of the ruins and the surrounding environment is carried out in order to characterize it on a dense point cloud. Then, a digital elevation model (DEM) was calculated in order to identify critical slope lines, which are significant to determine where the structure of the church was built. Our results can be used for the development of an architectural project and thus a virtual recreation of these archaeological ruins was performed. [ABSTRACT FROM AUTHOR]

The importance of three-dimensional (3D) point cloud technologies in the field of agriculture environmental research has increased in recent years. Obtaining dense and accurate 3D reconstructions of plants and urban areas provide useful information for remote sensing. In this paper, we propose a novel strategy for the enhancement of 3D point clouds from a single 4D light field (LF) image. Using a light field camera in this way creates an easy way for obtaining 3D point clouds from one snapshot and enabling diversity in monitoring and modelling applications for remote sensing. Considering an LF image and associated depth map as an input, we first apply histogram equalization and histogram stretching to enhance the separation between depth planes. We then apply multi-modal edge detection by using feature matching and fuzzy logic from the central sub-aperture LF image and the depth map. These two steps of depth map enhancement are significant parts of our novelty for this work. After combing the two previous steps and transforming the point–plane correspondence, we can obtain the 3D point cloud. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. The results showed that our method can reliably mitigate noise and had the highest level of detail compared to other existing methods. [ABSTRACT FROM AUTHOR]

With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that the number and resolution of images are enormous, which brings significant computational cost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In this paper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchically merges partial structures to reconstruct the full scene. Through image clustering, an overlapping relationship between image subsets is established. With the overlapping relationship, we propose a similarity transformation estimation method based on joint camera poses of common images. Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybrid optimization method to make the merged complete scene seamless. The advantage of the proposed method is a significant efficiency improvement without a marginal loss in accuracy. Experimental results on the Qinling dataset captured over Qinling mountain covering 57 square kilometers demonstrate the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions. [ABSTRACT FROM AUTHOR]