Search

We present a new learning-based framework S-3D-RCNN that can recover accurate object orientation in SO(3) and simultaneously predict implicit rigid shapes from stereo RGB images. For orientation estimation, in contrast to previous studies that map local appearance to observation angles, we propose a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs). This approach features a deep model that transforms perceived intensities from one or two views to object part coordinates to achieve direct egocentric object orientation estimation in the camera coordinate system. To further achieve finer description inside 3D bounding boxes, we investigate the implicit shape estimation problem from stereo images. We model visible object surfaces by designing a point-based representation, augmenting IGRs to explicitly address the unseen surface hallucination problem. Extensive experiments validate the effectiveness of the proposed IGRs, and S-3D-RCNN achieves superior 3D scene understanding performance. We also designed new metrics on the KITTI benchmark for our evaluation of implicit shape estimation. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi'an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy. [ABSTRACT FROM AUTHOR]

Facial expression is an important way to reflect human emotions and it represents a dynamic deformation process. Analyzing facial movements is an effective means of understanding expressions. However, there is currently a lack of methods capable of analyzing the dynamic details of full-field deformation in expressions. In this paper, in order to enable effective dynamic analysis of expressions, a classic optical measuring method called stereo digital image correlation (stereo-DIC or 3D-DIC) is employed to analyze the deformation fields of facial expressions. The forming processes of six basic facial expressions of certain experimental subjects are analyzed through the displacement and strain fields calculated by 3D-DIC. The displacement fields of each expression exhibit strong consistency with the action units (AUs) defined by the classical Facial Action Coding System (FACS). Moreover, it is shown that the gradient of the displacement, i.e., the strain fields, offers special advantages in characterizing facial expressions due to their localized nature, effectively sensing the nuanced dynamics of facial movements. By processing extensive data, this study demonstrates two featured regions in six basic expressions, one where deformation begins and the other where deformation is most severe. Based on these two regions, the temporal evolutions of the six basic expressions are discussed. The presented investigations demonstrate the superior performance of 3D-DIC in the quantitative analysis of facial expressions. The proposed analytical strategy might have potential value in objectively characterizing human expressions based on quantitative measurement. [ABSTRACT FROM AUTHOR]

Due to the presence of an additional glass pane between the camera and the scene, an additional reflection scene is captured in the image apart from the desired object sometimes. Images are more often captured from mobile handsets these days which have multiple cameras. This paper gives the advantage of multiple cameras. There exists a disparity and varied field of view when images are captured with multiple cameras. We use these two factors to act as a cue to remove reflection, as reflection intensity across the image pairs change with different field-of-view. The proposed method is robust and convenient to implement as it does not require an additional hardware, for example, light field camera for stereo images. Also, it does not make assumptions about the appearance or intensity of reflection. [ABSTRACT FROM AUTHOR]

Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D technologies to the next level. Depth estimation of multi-exposure stereo image sequences is an essential task in the development of cost-effective 3D HDR video content. In this paper, we develop a deep architecture for multi-exposure stereo depth estimation. The proposed architecture has two novel components. First, the stereo matching technique used in traditional stereo depth estimation is revamped. For the stereo depth estimation component of our architecture, a mono-to-stereo transfer learning approach is deployed. The proposed formulation circumvents the cost volume construction requirement, which is replaced by a dual-encoder single-decoder CNN with different weights for feature fusion. EfficientNet-based blocks are used to learn the disparity. Secondly, we combine disparity maps obtained from the stereo images at different exposure levels using a robust disparity feature fusion approach. The disparity maps obtained at different exposures are merged using weight maps calculated for different quality measures. The final predicted disparity map obtained is more robust and retains best features that preserve the depth discontinuities. The proposed CNN offers flexibility to train using standard dynamic range stereo data or with multi-exposure low dynamic range stereo sequences. In terms of performance, the proposed model surpasses state-of-the-art monocular and stereo depth estimation methods, both quantitatively and qualitatively, on challenging Scene flow and differently exposed Middlebury stereo datasets. The architecture performs exceedingly well on complex natural scenes, demonstrating its usefulness for diverse 3D HDR applications. [ABSTRACT FROM AUTHOR]

Acquiring disparity maps by dense stereo matching is one of the most important methods for producing digital surface models. However, the characteristics of optical satellite imagery, including significant occlusions and long baselines, increase the challenges of dense matching. In this study, we propose an end-to-end edge-guided multi-scale matching network (EGMS-Net) tailored for optical satellite stereo image pairs. Using small convolutional filters and residual blocks, the EGMS-Net captures rich high-frequency signals during the initial feature extraction phase. Subsequently, pyramid features are derived through efficient down-sampling and consolidated into cost volumes. To regularize these cost volumes, we design a top–down multi-scale fusion network that integrates an attention mechanism. Finally, we innovate the use of trainable guided filter layers in disparity refinement to improve edge detail recovery. The network is trained and evaluated using the Urban Semantic 3D and WHU-Stereo datasets, with subsequent analysis of the disparity maps. The results show that the EGMS-Net provides superior results, achieving endpoint errors of 1.515 and 2.459 pixels, respectively. In challenging scenarios, particularly in regions with textureless surfaces and dense buildings, our network consistently delivers satisfactory matching performance. In addition, EGMS-Net reduces training time and increases network efficiency, improving overall results. [ABSTRACT FROM AUTHOR]

Real-time stereo matching with high accuracy is a dynamic research topic; it is attractive in diverse computer vision applications. This paper presents a stereo-matching algorithm that produces high-quality disparity map while maintaining real-time performance. The proposed stereo-matching method is based on three per-pixel difference measurements with adjustment elements. The absolute differences and the gradient matching are combined with a colour-weighted extension of complete rank transform to reduce the effect of radiometric distortion. The disparity calculation is realized using improved dynamic programming that optimizes along and across all scanlines. It solves the inter-scanline inconsistency problem and increases the matching accuracy. The proposed algorithm is implemented on parallel high-performance graphic hardware using the Compute Unified Device Architecture to reach over 240 million disparity evaluations per second. The processing speed of our algorithm reaches 98 frames per second on 240 × 320-pixel images and 32 disparity levels. Our method ranks fourth in terms of accuracy and runtime for quarter-resolution images in the Middlebury stereo benchmark. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow. [ABSTRACT FROM AUTHOR]

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons. [ABSTRACT FROM AUTHOR]

This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations. [ABSTRACT FROM AUTHOR]

We present a marker-less AR/DR system that can replace the arm of the user with a virtual bionic prosthesis in real time including finger tracking. For this, we use a mixed reality HMD that provides the user with a stereo image based on video-see-through (VST). We apply chroma-keying to remove the user's arm from each captured image and input reconstructed background information into the removed pixels. Before rendering the prosthesis model into the image, we re-target motion capture data of the user's hand to the kinematic skeleton of the prosthesis to match the current hand pose. This system opens new research possibilities on self- and other-perception of bionic bodies. In a first evaluation study of the system, we propose that users perceive the virtual prosthesis model as a part of their body (i.e., that they experience a sense of ownership). We tested this assumption in a laboratory study with 27 individuals who used the system to perform a series of simple tasks in AR with their prosthesis. We measured body ownership and other measures with self-reports. In support of the hypothesis, users experienced a sense of body ownership. Also, a feeling of self-presence is induced during the task, and participants rated the overall experience as positive. [ABSTRACT FROM AUTHOR]

Background: The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. Results: In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1 mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = 1.5 ∗ 10 - 29 ) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. Conclusion: This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions. [ABSTRACT FROM AUTHOR]

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often contain noise, occluded regions, untextured areas, and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and non-negative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder–decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain an accuracy of 1.115 pixels in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images. [ABSTRACT FROM AUTHOR]

Fine‐scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very‐high‐resolution satellite stereo images from WorldView‐2, WorldView‐3, and Pléiades‐HR 1A/1B, we produced a six‐year time series (2017–2022) of spatially continuous digital elevation models for an 874 km2 study area over Grand Mesa, Colorado. We generated high‐resolution stereo snow depth maps that capture intra‐ and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near‐contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies. Plain Language Summary: Detailed observations of snow depth can help us better understand how much water is stored as snow during important times of the year. We used high‐resolution images from commercial satellites to create detailed maps of snow‐covered surfaces for a study site in Colorado. Using a technique called stereo photogrammetry, we created precise three‐dimensional models of surface elevation from these images. By subtracting a snow‐free summer ground surface model from the winter snow surface models, we estimated snow depth over large areas and multiple years. Our satellite snow depth estimates agreed with snow depth measurements from airborne lidar and field campaigns. This satellite stereo approach helps us understand how mountain snow depth varies from year to year, providing valuable information to improve models and decisions for water resources management. Key Points: Satellite stereo photogrammetry offers repeat, spatially continuous, high‐resolution snow depth measurements over large areasStereo snow depth measurements are within ∼0.13–0.33 m of near‐contemporaneous airborne lidar and in situ measurementsStereo snow depth captures detailed intra‐ and interannual snow depth variability in low and high snow years [ABSTRACT FROM AUTHOR]

This paper proposes and experimentally evaluates, a reconfigurable intelligent surface (RIS) control system for multiple users using stereo camera images. The aim is to enable fast and fair received signal strength (RSS) beamforming for multiple users. In the proposed system, a stereo camera is mounted on top of the RIS. Human bodies are recognized from the obtained images. Subsequently, the beam directions and the phase of the RIS are calculated for the estimated positions. The stereo camera‐based scheme enables fast beamforming because channel state information estimation is omitted. Moreover, identical RSS beamforming is enabled by calculating the weighted sums of the RIS phases for multiple users utilizing distance and angle information of users from the RIS obtained using the stereo camera. Experiments are conducted in an anechoic chamber using RIS operating at 28 GHz. The efficacy of the RIS control system using stereo camera images and its ability to increase the RSS fairly for multiple users is validated via simulations and experiments. [ABSTRACT FROM AUTHOR]

The deformation detection of large machinery is usually achieved using threedimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [-0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results. [ABSTRACT FROM AUTHOR]

Accurately estimating the collapse height of buildings during earthquakes holds great significance. Acquiring precise height information of buildings using dual-phase high-resolution stereo satellite images (HRSSIs) requires high-precision ground control points, which are difficult to obtain in earthquake-stricken areas. Spaceborne laser data has been validated to significantly improve the vertical accuracy of HRSSIs. Therefore, this paper first proposes the application of a combined stereo adjustment model in building collapse estimation and thoroughly verifies its feasibility. Firstly, the laser altimetry points (LAPs) acquired by GF-7 and ICESat-1 serve as elevation control points (ECPs), and the tie points from HRSSIs were used as constraint to conduct combined adjustment to improve the consistency of stereoscopic positioning accuracy between HRSSIs before and after the earthquake. Subsequently, a footprints-to-conners, coarse-to-fine, automatic building corners extraction method was performed. Avoiding the time-consuming generation of large-scale DSM while also improving the accuracy of building collapse assessment. Finally, according to the height change of building corners calculated from HRSSIs, building damage assessment was completed. According to the above methods, independent experiments were carried out respectively for the Oaxaca earthquake in Mexico in 2020 and the Kahramanmaras earthquake in Türkiye in 2023. GF-7 HRSSIs was used to verify the accuracy and stability of the proposed method for 3D assessment of building damage. The results indicate that the RMSE of elevation positioning accuracy has been greatly reduced in both regions, ranging from 10.29 m to 0.95 m and from 6.67 m to 0.99 m, respectively. This accuracy guarantees the feasibility of detecting the building floor-level collapse. And 2541 building corner points were extracted from two disaster areas, with correct corner points accounting for 85.18%. A total of 520 buildings were correctly extracted corner points, with an accuracy rate of 94.12% for assessing the degree of damage. [ABSTRACT FROM AUTHOR]

With 3D products being widely applied, more attention has focused on studying stereo image super-resolution (SR). Current stereo image SR studies mainly aim to improve the performance by the additional information from a pair of low-resolution stereo images. However, it is challenging for stereo image SR to fully exploit self-similarity information from its own image and parallax information between stereo image pairs. In line with these challenges, this paper presents a Two-Branch Network (TBNet) to integrate self-similarity information and parallax information for SR. In the TBnet, a stereo parallax transfer module with an encoder–decoder structure was first proposed to sufficiently transfer multi-scale parallax information and preserve the stereo consistency between stereo images. This paper further presented a residual pyramid self-attention module to employ self-similarity information to take advantage of self-predictive power. Finally, extensive experiments demonstrate the superiority of our model over the state-of-the-art performance in terms of objective and perceptual quality and the accuracy of disparity estimation. [ABSTRACT FROM AUTHOR]

The panchromatic stereo images of SPOT 6 were assessed with respect to three-dimensional georeferencing accuracy using sensor-dependent orientation model for the first time. The panchromatic images were acquired in stereo mode with ~1.50 m ground sampling distance at nadir-view, covering Zonguldak (Türkiye) test site. The sensor-dependent orientation model developed for the Pléiades 1A & 1B and Göktürk 1 satellites were preferred since the imaging geometry of SPOT 6 is mostly identical with them. However, the quaternion estimation was modified in accordance with the SPOT 6's metadata. Two point sets, consisting of ground control points (GCPs) and independent check points (ICPs), were established by in-field GNSS survey, and adjusted georeferencing accuracy was estimated to be approximately ±1 cm and ±1.50 m (±1 GSD) at GCPs and ICPs, respectively. All process was carried out by Geo3o1 tool of GeoEtrim derived in MATLAB environment. [ABSTRACT FROM AUTHOR]

Glaciers are crucial sources of freshwater in particular for the arid lowlands surrounding High Mountain Asia. To better constrain glacio-hydrological models, annual, or even better, seasonal information about glacier mass changes is highly beneficial. In this study, we evaluate the suitability of very-high-resolution Pléiades digital elevation models (DEMs) to measure glacier mass balance at annual and seasonal scales in two regions of High Mountain Asia (Muztagh Ata in Eastern Pamirs and parts of western Nyainqêntanglha, south-central Tibetan Plateau), where recent estimates have shown contrasting glacier behaviour. The average annual mass balance in Muztagh Ata between 2019 and 2022 was -0.07 ± 0.20 m w.e. a -1 , suggesting the continuation of a recent phase of slight mass loss following a prolonged period of balanced mass budgets previously observed. The mean annual mass balance in western Nyainqêntanglha was highly negative for the same period (-0.60 ± 0.15 m w.e. a -1), suggesting increased mass loss rates compared to the approximately previous 5 decades. The 2022 winter (+0.13 ± 0.24 m w.e.) and summer (-0.35 ± 0.15 m w.e.) mass budgets in Muztagh Ata and western Nyainqêntanglha (-0.03 ± 0.27 m w.e. in winter; -0.63 ± 0.07 m w.e. in summer) suggest winter- and summer-accumulation-type regimes, respectively. We support our findings by implementing the Sentinel-1-based Glacier Index to identify the firn and wet-snow areas on glaciers and characterize the accumulation type. The good match between the geodetic and Glacier Index results supports the potential of very-high-resolution Pléiades data to monitor mass balance at short timescales and improves our understanding of glacier accumulation regimes across High Mountain Asia. [ABSTRACT FROM AUTHOR]

The behavior of wind turbine wake is unique and has a distinct pattern that often carries crucial information on the nature of wake propagation. This information plays a vital role in dictating the wind turbine wake model and subsequently in the design and placement of multirotor system. In most cases, either analytical or numerical, the representation of wind turbine wake is expressed as a streamwise flow deficit at different downstream locations. Though the result provides valuable information, the representation often lacks three-dimensional characterization, thus ignoring several influential factors that could potentially define the extent of propagation. In this aspect, an in-depth flow field mapping and assessment have been carried out behind a model horizontal-axis wind turbine using stereo particle image velocimetry (SPIV) under low tip speed ratio conditions. The three-component mapping using the time-resolved SPIV data helped to understand the notion of the wake's helical behavior and its relationship with the velocity deficit. Apart from the Gaussian-like (skew) distribution of the streamwise velocity component, the stereo assessment reveals the formation of two opposite crests supporting the helical analogy. Toward the end, a comparative assessment with the existing analytical models have been carried out. [ABSTRACT FROM AUTHOR]

Laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) contain a lot of noise, which necessitates the requirement for a signal photon extraction method. In this study, we propose a density clustering method, which combines slope and elevation information from optical stereo images and adaptively adjusts the neighborhood search direction in the along-track direction. The local classification density threshold was calculated adaptively according to the uneven spatial distribution of noise and signal density, and reliable surface signal points were extracted. The performance of the algorithm was validated for strong and weak beam laser altimetry data using optical stereo images with different resolutions and positioning accuracies. The results were compared qualitatively and quantitatively with those obtained using the ATL08 algorithm. The signal extraction quality was better than that of the ATL08 algorithm for steep slope and low signal-to-noise ratio (SNR) regions. The proposed method can better balance the relationship between recall and precision, and its F1-score was higher than that of the ATL08 algorithm. The method can accurately extract continuous and reliable surface signals for both strong and weak beams among different terrains and land cover types. [ABSTRACT FROM AUTHOR]

In this study, the flow fields of two‐stage impellers, consisting of combinations of different impeller types on one shaft, are investigated using stereo particle image velocimetry. Based on the results of torque measurements, diameters for different impellers are determined in order to achieve matching power input of the individual turbines. When implemented adequately, both selected impellers are able to contribute their respective characteristics, resulting in flow fields that differ significantly from those induced by single‐stage impellers as well as two‐stage setups consisting of identical impellers. [ABSTRACT FROM AUTHOR]

In this paper, a sweeping jet was applied to control the afterbody vortices behind a 30° slanted-base cylinder at a Reynolds number of 87 000. Phase-locked stereo particle image velocimetry measurements were conducted during a sweeping jet actuation cycle, and insights of the complex three-dimensional interaction process between the sweeping jet and the afterbody vortices have been obtained for the first time. It is found that in the near-field of the jet orifice (x/c < 0.3), the jet swings from side to side over the endplate between the afterbody vortex pair. An intersection between them around x/c = 0.3 causes the sweeping jet to take on a zigzag pattern as it propagates downstream along the slanted surface. From x/c = 0.6 onwards, the intensity of this interaction decreases as the afterbody vortices become detached from the base. Nevertheless, the sweeping jet continues invading the afterbody vortices from their inner side and pushes its way outwards from the underside of the afterbody vortices causing them to weaken further. Furthermore, the interaction between sweeping jets and afterbody vortices results in a cyclic variation in the circulation of afterbody vortices, which increases in magnitude at downstream locations. Except for the phases where a merger between the afterbody vortex and the sweeping occurs, a reduction in the circulation is observed. Finally, the level of interaction between sweeping jets and afterbody vortices intensifies as the strength of the sweeping jet increases resulting in the afterbody vortices being penetrated by the sweeping jet and their regular shape being momentarily destroyed. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions. [ABSTRACT FROM AUTHOR]

We present a novel and high-performance framework for 3D object detection using stereo vision. This framework incorporates direct instance depth estimation efficiently, improving the accuracy of the final 3D object detection. Instead of detecting objects separately in the left and right images of a stereo display, we exploit a modified 2D object detector that takes only the left image as input to generate union 2D bounding boxes for both images, and to predict the depth of the 3D box center for each object. Using the union 2D boxes, we propose a direct instance-level depth estimation network, taking the estimated depth as guidance, to predict the desired depths for pixels belonging to an object from a small search range. This approach greatly improves the efficiency and accuracy of 3D detection. Moreover, we design an adaptive spatial feature aggregation module that can weaken the effect of background points and automatically integrate important instance features to achieve accurate 3D object localization. Our method outperforms current state-of-the-art stereo-based 3D detection methods on the KITTI benchmark dataset, and it can efficiently employ a shared model for 3D multi-class detection. Code will be available at https://github.com/xjtuwh/iDepNet/tree/master. [ABSTRACT FROM AUTHOR]

With stereo cameras becoming widely used in invasive surgery systems, stereo endoscopic images provide important depth information for delicate surgical tasks. However, the small size of sensors and their limited lighting conditions lead to low-quality and low-resolution endoscopic images and videos. In this paper, we propose a stereo endoscopic video super-resolution method using transformer with a hybrid attention mechanism named HA-VSR. Stereo video SR aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) videos. In our method, the stereo correspondence and temporal correspondence are incorporated into the HA-VSR model. Specifically, the Swin transformer architecture is utilized in proposed framework with hybrid attention mechanisms. The parallel attention mechanism is utilized by using the symmetry and consistency of left and right images, and the temporal attention mechanism is utilized by using the consistency of consecutive frames. Detailed quantitative evaluation and experiments on two datasets show the proposed model achieves advanced SR reconstruction performance, showing that the proposed stereo VSR framework outperforms alternative approaches. [ABSTRACT FROM AUTHOR]

Even though the simulations used to describe the failure of laminates are becoming more and more predictive, complex testing under multiaxial loadings is still required to validate the design of structural parts in a wide range of industrial domains. It is thus essential to assess the actual boundary conditions to allow for an objective comparison between testing and calculations, in particular since the structural tests are complex and often leads to buckling. Therefore, accurate estimation of force and moment fluxes applied to the specimen is critical. In this context, stereo digital image correlation (SDIC) has proven to be an important measurement tool and provides very well‐resolved surface displacement fields, but the exploitation of such measurements to calculate fluxes remains problematic when testing composites. The first objective of this study is both to reduce the uncertainty associated with fluxes determination on a complex test and to simplify the extraction process with respect to existing procedures. The second objective is to make this methodology robust to geometrically non‐linear deformations. In this paper, we propose a new methodology that extracts minimal boundary conditions in the form of 3D mechanically admissible displacements fields. The approach developed uses a finite element SDIC (FE‐SDIC) method regularized by means of mechanical behaviour admissibility equations. Results show that the new methodology outputs much more accurate fluxes than classical data generated from multiple differentiations of the displacement fields. Excellent noise robustness is obtained and quantified. Numerical predictions have been satisfactorily compared with experimental data from one structural‐scale composite specimen under complex testing. [ABSTRACT FROM AUTHOR]

Recently stereo image deraining has attracted lots of attention due to its superiority of abundant information from cross views. Exploring interaction information across stereo views is the key to improving the performance of stereo image deraining. In this paper, we design a general coarse-to-fine deraining framework for stereo rain streak and raindrop removal, called CDINet, comprising a stereo rain removal subnet and a stereo detail recovery subnet to restore images progressively. Two types of interaction modules are devised to explore interaction information for rain removal and detail recovery, respectively. Specifically, a global context interaction module is proposed to learn long-range dependencies of stereo images and remove rain by utilizing stereo structural information. A local detail interaction module is designed to model local contextual correlation, which aims at restoring the detail information by using neighborhood information from cross views. Extensive experiments are conducted on the two datasets including a synthetic rain streak removal dataset (RainKITTI) and a real raindrop removal dataset (Stereo Waterdrop), which demonstrates that our method sets new state-of-the-art deraining performance in terms of both quantitative and qualitative metrics with faster speed. [ABSTRACT FROM AUTHOR]

Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics. Additionally, associating individual tree positions with dendrometric variables like diameter, taper, and species can provide data for highly optimized, site-specific silvicultural prescriptions designed to achieve diverse management objectives. Aerial photogrammetry has proven effective for mapping individual trees; however, its utility is limited due to the inability to directly estimate many dendrometric variables. In contrast, terrestrial mapping methods can directly observe essential individual tree characteristics, such as diameter, but their mapping accuracy is governed by the accuracy of the global satellite navigation system (GNSS) receiver and the density of the canopy obstructions between the receiver and the satellite constellation. In this paper, we introduce an integrated approach that combines a camera-based motion and tree detection system with GNSS positioning, yielding a stem map with twice the accuracy of using a consumer-grade GNSS receiver alone. We demonstrate that large-scale stem maps can be generated in real time, achieving a root mean squared position error of 2.16 m. We offer an in-depth explanation of a visual egomotion estimation algorithm designed to enhance the local consistency of GNSS-based positioning. Additionally, we present a least squares minimization technique for concurrently optimizing the pose track and the positions of individual tree stem[s]. [ABSTRACT FROM AUTHOR]

Model-based stereo vision methods can estimate the 6D poses of rigid objects. They can help robots to achieve a target grip in complex home environments. This study presents a novel approach, called the variable photo-model method, to estimate the pose and size of an unknown object using a single photo of the same category. By employing a pre-trained You Only Look Once (YOLO) v4 weight for object detection and 2D model generation in the photo, the method converts the segmented 2D photo-model into 3D flat photo-models assuming different sizes and poses. Through perspective projection and model matching, the method finds the best match between the model and the actual object in the captured stereo images. The matching fitness function is optimized using a genetic algorithm (GA). Unlike data-driven approaches, this approach does not require multiple photos or pre-training time for single object pose recognition, making it more versatile. Indoor experiments demonstrate the effectiveness of the variable photo-model method in estimating the pose and size of the target objects within the same class. The findings of this study have practical implications for object detection prior to robotic grasping, particularly due to its ease of application and the limited data required. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes. [ABSTRACT FROM AUTHOR]

Recovering an outdoor environment's surface mesh is vital for an agricultural robot during task planning and remote visualization. Image-based dense 3D reconstruction is sensitive to large movements between adjacent frames and the quality of the estimated depth maps. Our proposed solution for these problems is based on a newly-designed panoramic stereo camera along with a hybrid novel software framework that consists of three fusion modules: disparity fusion, pose fusion, and volumetric fusion. The panoramic stereo camera with a pentagon shape consists of 5 stereo vision camera pairs to stream synchronized panoramic stereo images for the following three fusion modules. In the disparity fusion module, rectified stereo images produce the initial disparity maps using multiple stereo vision algorithms. Then, these initial disparity maps, along with the intensity images, are input into a disparity fusion network to produce refined disparity maps. Next, the refined disparity maps are converted into full-view (360 °) point clouds or single-view (72 °) point clouds for the pose fusion module. The pose fusion module adopts a two-stage global-coarse-to-local-fine strategy. In the first stage, each pair of full-view point clouds is registered by a global point cloud matching algorithm to estimate the transformation for a global pose graph's edge, which effectively implements loop closure. In the second stage, a local point cloud matching algorithm is used to match single-view point clouds in different nodes. Next, we locally refine the poses of all corresponding edges in the global pose graph using three proposed rules, thus constructing a refined pose graph. The refined pose graph is optimized to produce a global pose trajectory for volumetric fusion. In the volumetric fusion module, the global poses of all the nodes are used to integrate the single-view point clouds into the volume to produce the mesh of the whole garden. The proposed framework and its three fusion modules are tested on a real outdoor garden dataset to show the superiority of the performance. The whole pipeline takes about 4 min on a desktop computer to process the real garden dataset, which is available at: https://github.com/Canpu999/Trimbot-Wageningen-SLAM-Dataset. [ABSTRACT FROM AUTHOR]

The article addresses the issues of improving the accuracy and reliability in identifying stereo images via a priori estimation of the specific identification error arising from the asymmetry of reference image intensity near the analyzed point (within the identification "window"). The mechanism underlying this error is examined, and a method for its prediction is developed. The work methodology includes a geometric analysis, an analytical description of the imaging process, and computer simulation. A variant of stereoscopic television system geometry is considered, i.e., with identical cameras whose optical axes are parallel to each other and orthogonal to the stereoscopic base. Here, the position of the reference relief plane serves as a priori information for finding a solution. A procedure is developed for determining the intensity center, which is used in the identification error prediction method. Using computer simulation, the author demonstrates the effect produced by the intensity center asymmetry relative to the "window" center on the identification error and the possibility of predicting it. The developed method can be used to decide whether to perform a search procedure for the second stereo image or to reject the analyzed point in case of an unacceptably large predicted identification error, as well as to limit the search interval for the second image and adjust the "window" size if necessary. [ABSTRACT FROM AUTHOR]

Algorithms for converting 2D to 3D are gaining importance following the hiatus brought about by the discontinuation of 3D TV production; this is due to the high availability and popularity of virtual reality systems that use stereo vision. In this paper, several depth image-based rendering (DIBR) approaches using state-of-the-art single-frame depth generation neural networks and inpaint algorithms are proposed and validated, including a novel very fast inpaint (FAST). FAST significantly exceeds the speed of currently used inpaint algorithms by reducing computational complexity, without degrading the quality of the resulting image. The role of the inpaint algorithm is to fill in missing pixels in the stereo pair estimated by DIBR. Missing estimated pixels appear at the boundaries of areas that differ significantly in their estimated distance from the observer. In addition, we propose parameterizing DIBR using a singular, easy-to-interpret adaptable parameter that can be adjusted online according to the preferences of the user who views the visualization. This single parameter governs both the camera parameters and the maximum binocular disparity. The proposed solutions are also compared with a fully automatic 2D to 3D mapping solution. The algorithm proposed in this work, which features intuitive disparity steering, the foundational deep neural network MiDaS, and the FAST inpaint algorithm, received considerable acclaim from evaluators. The mean absolute error of the proposed solution does not contain statistically significant differences from state-of-the-art approaches like Deep3D and other DIBR-based approaches using different inpaint functions. Since both the source codes and the generated videos are available for download, all experiments can be reproduced, and one can apply our algorithm to any selected video or single image to convert it. [ABSTRACT FROM AUTHOR]

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous regions in an identify-rectify manner. Hard-to-train and incorrect warping samples are first discovered by two strategies, (1) pruning the network to reveal low-confident predictions; and (2) bidirectionally matching between stereo images to allow the discovery of improper mapping. These regions are then inpainted to form the final pseudo-stereo. With the aid of this extra input, a preferable 3D reconstruction can be easily obtained, and our method can work with arbitrary 3D representations. Extensive experiments show that our method outperforms state-of-the-art single-view view synthesis methods and stereo synthesis methods. [ABSTRACT FROM AUTHOR]

Many objectives in geoscience and engineering require Earth surface elevations at greater temporospatial resolution and coverage than are currently available. This may be achieved with stereo imagery from large constellations of "small sats", such as PlanetScope Doves. Obtaining Digital Surface Models (DSMs) of sufficient quality from these images is challenging due to their lower resolution and weaker stereo geometry relative to stereo mode satellites such as WorldView. The quality can be improved by utilizing their much larger numbers of repeat images, but this requires effective stereopair selection. To determine the stereo geometries required for obtaining quality DSMs from PlanetScope Dove imagery, we apply a new methodology for generating simulated stereo images of varying geometries using adjusted orientation parameters obtained by a self-calibrating bundle adjustment and validated by comparing the resulting rigorous sensor and rational function models. The accuracies of simulated stereo and multi-pair DSMs are then assessed through comparison to a reference DSM, providing the relationship between specific imaging geometries and DSM quality. Our results provide a basis for automated stereo imagery selection to enable large-scale DSM production from PlanetScope Dove imagery. Our methodology can be applied to other sources of stereo imagery and designing future satellite missions. In the future, we will further develop multi-pair matching algorithms for generating DSMs with Dove Classic images to improve both accuracy and quality that are otherwise limited by the weak stereo geometry of single stereo pairs. [ABSTRACT FROM AUTHOR]

The properties of the wake flow behind concave curved cylinders have been investigated analyzing simultaneously the mean velocity profiles extracted along concentric arcs parallel to the cylinder axis and the dynamically most relevant coherent structures obtained by spectral proper orthogonal decomposition. These analyzes have allowed us to determine the position, the extension, and the evolution of the different wake regimes. The velocity measurements have been obtained through the stereo particle image velocimetry (stereo-PIV) technique. The study considers various Reynolds numbers, based on cylinder diameter (240 < R e < 840), and curvatures. The quarter-of-ring cylinder is first analyzed. Near the cylinder root, the flow exhibits a topology dominated by an oblique vortex shedding, contrary to what is observed in the literature for similar geometric configurations. We attribute this disagreement to differences in the treatment of the free-end conditions that play a role in triggering the shedding regime. We find that the local shedding inclination is driven by the axial velocity, whereas its extension and wavelength depend mainly on the Reynolds number. The near free-end region presents, instead, two counter-rotating standing vortices that induce a cross-wise velocity directed toward the cylinder root and a tip vortex that expands evolving downstream. As the Reynolds number increases, the wake presents irregularities and the shedding spreads becoming nearly normal to the incoming flow. At smaller curvatures, the cylinder free-end becoming more inclined with respect to the incoming flow, and the free-end effects are enhanced. The interaction with the vortex sheets of the standing vortices that develop at the leeward side weakens. As a consequence, these vortices stretch in the stream-wise direction giving rise to the trailing vortices that stem from the cylinder surface and affect the cross-wise velocity distributions. [ABSTRACT FROM AUTHOR]

Wind tunnel testing and accompanying numerical analyses are the essential methods to improve and optimize the aerodynamic design of helicopters. The focus is on improving the aerodynamic performance and the handling qualities. Of special interest is the parasitic drag as a major factor for the power consumption and the wake flow caused by the rotor head, which can interact with the helicopter tail and influence stability. For this purpose, design modifications and their influence on the aerodynamic characteristics of the helicopter as well as on the flow field are investigated. The design modifications are evaluated by force and moment measurements, stereo particle image velocimetry, and high fidelity computational analysis. This allows us to compare the different investigation methods and design configurations to each other. The investigations are performed on the Kopter AW09, a 2.8-t-class single engine utility helicopter. Design modifications of the upper fuselage are considered for the presented investigations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been successful for tasks such as object detection; however, they involve time-consuming processes. Therefore, there are difficulties in applying these CNNs to autonomous driving. Moreover, most autonomous driving technologies require both object detection and distance prediction. However, CNNs that predict distance involve more time-consuming processes than object detection models. In addition, the applications for autonomous driving require object detection and distance prediction accuracy. This paper proposes an end-to-end trainable CNN that can meet these requirements. The proposed CNN accurately implements object detection and distance prediction in real time using stereo images. We demonstrate the superiority of the proposed CNN using stereo images from the KITTI 3D object detection dataset. [ABSTRACT FROM AUTHOR]

The increased availability of high-resolution and high-quality digital elevation models (DEMs) allows for the investigation of small-scale glaciological changes and improved precision in geodetic mass-balance estimates. However, high precision and careful methodological choices are required to retrieve glacier-wide mass changes at annual to seasonal time scales. Here, we used a 7-year time series of 12 DEMs of the Glacier d'Argentière, in the French Alps, derived from the Pléiades optical satellites to assess the ability of sub-metre stereoscopic satellite images to retrieve annual-to-seasonal mass balances. We found good agreement between the five annual and the four winter mass-balance values estimated using a geodetic method and those of in situ glaciological measurements: mean values via the geodetic method are −0.66 m w.e. and 1.47 m w.e. for annual and winter balances, respectively; mean absolute discrepancies are 0.25 m w.e. (annual) and 0.36 m w.e. (winter). Our study identified three main limitations of this methodology: (i) the intrinsic DEM precision; (ii) the lack of control over the satellite acquisition dates; and (iii) the density assumption. The consistency between the methods demonstrates the potential of short time-scale glacier mass-balance monitoring using very high-resolution satellite images. [ABSTRACT FROM AUTHOR]

Background: Conductor on Round Core (CORC) superconducting cable is made of YBa2Cu3O7-x(YBCO) superconducting tape wound along a copper core and used for wind superconducting magnets. Bending deformation is the main deformation mode during the winding process, which also caused the degradation of CORC cable superconducting performances. Objective: To directly measure the deformation characteristics of CORC cable during bending processes and reveal the origin of performance degradation. Methods: Based on the stereo-digital image correlation (stereo-DIC) method, a segmentation-aided stereo-digital image correlation (SA-Stereo-DIC) method has been proposed to optimize the results near the tape edge. Meanwhile, a binocular stereo DIC system has been established to enable accurate panoramic measurements of large deformations with high magnification by scanning the samples. Results: Deformation characteristics of CORC cable can be obtained, which in turn reveal the causes of performance degradation of CORC cable during bending processes. Conclusions: On the one hand, it effectively extends the use of the conventional stereo-DIC method, on the other hand, it lays the foundation for the subsequent optimization of the design during the application of CORC cable. [ABSTRACT FROM AUTHOR]

The spotlight image acquired by the Gaofen-3 satellite has a resolution of 1 m, which has great potential for 3-D localization. However, there have been no public reports on the 3-D localization accuracy evaluation of Gaofen-3 spotlight synthetic aperture radar (SAR) images. Here, three study areas were selected from this perspective, and the SAR spotlight stereo images of the study area were acquired using Gaofen-3. In the case of no ground control points (GCPs), based on the Rational Polynomial Coefficient (RPC) model, these images were used for initial 3-D localization; the plane accuracy was better than 10 m in general, and the elevation accuracy was worse than 37 m in general. Subsequently, the RPC model was optimized using geometric calibration technology, and the 3-D localization accuracy was assessed again. The elevation accuracy was significantly improved, which was generally better than 5 m. The plan accuracy was also improved, and it was generally better than 6 m. It can be seen that Gaofen-3 spotlight stereo images are of good quality, and high plane accuracy can be obtained even without GCPs. Geometric calibration technology improves the 3-D localization accuracy, and the elevation accuracy optimization effect is remarkable. Moreover, the optimization effect of plane accuracy is affected by the properties of stereo-image pairs. The optimization effect of plane accuracy is obvious for asymmetric stereo-image pairs, and the optimization effect of plane accuracy is general for symmetric stereo-image pairs. [ABSTRACT FROM AUTHOR]

As an important means of improving positioning accuracy, block adjustment has been used in the improvement and assessment of accuracy for the Chinese Gaofen-7 (GF-7) satellite. However, there is little research on what factors affect accuracy without ground control points (GCPs). The correlation between accuracy and the images participating in the adjustment is not clear. This paper proposes the correlation coefficients and canonical correlation analysis between five accuracy indicators and three sets of ten adjustment factors, including topographic factors, participating image factors, and tie points (TPs) factors, to quantify the influence of adjustment factors on accuracy. Block adjustment without GCPs for GF-7 stereo imagery is verified in three study areas to evaluate the relationship between accuracy and adjustment factors. The results show that block adjustment without GCPs can improve direct positioning accuracy with an average improvement of 1.27 m in the planar direction and 0.13 m in the elevation direction. Moreover, plane accuracy is more easily affected by three sets of factors, while the influence on elevation accuracy is more balanced. The set of TP factors has the greatest influence on accuracy, and the image overlap is more critical than the image coverage area, number, and time periods. Topographic factors also play an important role, and the influence of the elevation factor with the highest canonical correlation coefficient (−0.71) is more significant than the other two factors, roughness, and slope. The results provide a reference for the improvement of adjustment accuracy without GCPs, the reasonable selection of adjustment images, the optimization of TPs, and the strategy of the partition processing of large-area block adjustment for GF-7 stereo imagery. [ABSTRACT FROM AUTHOR]

The application of 3D technology is rapidly expanding, and stereoscopic imagery is typically used to display 3D data. However, compression, transmission, and other necessary processes may reduce the quality of these images. Stereo image quality assessment (SIQA) has gained more attention to guarantee that customers have a positive watching experience. In order to provide the highest level of experience, it is necessary to develop a quality evaluation mechanism for stereoscopic content that is both dependable and precise. A full-reference method for SIQA is presented in this paper. Compared to previous measures, this method gives users more freedom to use distorted pixel metrics and edge similarity. The binocular summation map is calculated by adding the left and right images for a stereo pair. Improved gradient similarity based distorted pixel measure (SGSDM) is used to calculate the quality of binocular summation. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score given by the database. The proposed method's efficacy is demonstrated by experimental comparisons. [ABSTRACT FROM AUTHOR]

This study focuses on solving the correspondence problem of multiple moving objects with similar appearances in stereoscopic videos. Specifically, we address the multi-camera correspondence problem by taking into account the pixel-level and feature-level stereo correspondences, and object-level cross-camera multiple object correspondence. Most correspondence algorithms rely on texture and color information of the stereo images, making it challenging to distinguish between similar-looking objects, such as ballet dancers and corporate employees wearing similar dresses, or farm animals such as chickens, ducks, and cows. However, by leveraging the low latency and high synchronization of high-speed cameras, we can perceive the phase and frequency differences between the movements of similar-looking objects. In this study, we propose using short-term velocities (STVs) of objects as motion features to determine the correspondence of multiple objects by calculating the similarity of STVs. To validate our approach, we conducted stereo correspondence experiments using markers attached to a metronome and natural hand movements to simulate simple and complex motion scenes. The experimental results demonstrate that our method achieved good performance in stereo correspondence. [ABSTRACT FROM AUTHOR]

Vision-based localization and mapping for agricultural environment is challenging due to unstructured scene with less distinguishable landmarks and more repetitive textures, scale drift caused by wide and distant view, illumination variations and abrupt movements. To address these challenges, we propose a scale-aware stereo direct odometry with online photometric calibration for agricultural environment. We first propose a scale-aware stereo direct image alignment method to explicitly capture the scale at tracking stage. Then we optimize the scale and affine brightness parameters of stereo frames to keep the scale consistency in the sliding window. Furthermore, we incorporate online photometric calibration to resist illumination changes and exposure differences of image sequence. The proposed system achieves new state-of-the-art results on two public agricultural datasets, demonstrating its effectiveness in challenging agricultural environments. [ABSTRACT FROM AUTHOR]

The work performance of stereo digital image correlation (stereo-DIC) technologies, especially the operating accuracy and reliability in field applications, is not fully understood. In this study, the key technologies of the field remote 3D displacement sensing of civil structures based on stereo-DIC have been proposed. An image correlation algorithm is incorporated in improving the matching accuracy of control points. An adaptive stereo-DIC extrinsic parameter calibration method is developed by fusing epipolar-geometry-based and homography-based methods. Furthermore, a reliable reference frame that does not require artificial markers is established based on Euclidean transformation, which facilitates in-plane and out-of-plane displacement monitoring for civil structures. Moreover, a camera motion correction is introduced by considering background points according to the camera motion model. With an experiment, the feasibility and accuracy of the proposed system are validated. Moreover, the system is applied to sense the dynamic operating displacement of a 2 MW wind turbine's blades. The results show the potential capability of the proposed stereo-DIC system in remote capturing the full-field 3D dynamic responses and health status of large-scale structures. [ABSTRACT FROM AUTHOR]

China's first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements' accuracies. The results indicated that the accuracy of GF-7 DEMMethod1 was the worst and that of GF-7 DEMMethod3 was superior to that of GF-7 DEMMethod2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets. [ABSTRACT FROM AUTHOR]