Search Results (134)

The identification of lithology and fluids in reservoirs is the key to the quantitative characterization of gas reservoirs. However, the Ma5₄¹ Member of the Majiagou Formation in the Ordos Basin is characterized by strong reservoir heterogeneity, variable lithologic components and complex gas–water relationships. This leads to severe overlapping of conventional logging responses, posing significant challenges to detailed reservoir evaluation. Taking the Ma5₄¹ Member in the central Shaanbei Slope of the Ordos Basin as the research object, this study adopts the logging curve superposition and reconstruction method to quantitatively identify reservoir lithology and fluid properties, and establishes a set of identification standards for lithology-fluid logging curve superposition and reconstruction. The results show that the lithology identification plate constructed by introducing new parameters eliminates dimensional differences and effectively highlights the response characteristics of different lithologies. It can rapidly and effectively identify limestone, limy dolomite, dolomite, argillaceous dolomite, and mudstone with an identification accuracy exceeding 90% and an average accuracy of over 92%. In terms of fluid identification, the constructed ΔΦ₃–ΔΦ₄–ΔΦ₅ 3D plate successfully achieved the stereoscopic differentiation of gas layers, gas-bearing water layers, water layers, and dry layers. The gas layer identification accuracy reached 93.9%, which is significantly superior to the traditional 2D crossplot method. Applying this model to the plane prediction of lithology in the Ma5₄¹ Member of the study area, it was found that the lithology distribution features “pure in the east and mixed in the west.” The central-eastern and southeastern parts of the study area mainly develop high-quality dolomite and limy dolomite reservoirs, making them favorable areas for natural gas exploration. This study provides effective technical support for the quantitative identification of lithology and fluids in non-cored well sections and improves regional exploration and development efficiency. Full article

We present a stereoscopic analysis of coronal streamer deflection induced by a CME-driven shock, utilizing multi-viewpoint coronagraph observations from STEREO-Ahead, STEREO-Behind, and SOHO. Driven by the continuous impact of the shock, the streamer deflection propagates outward, exhibiting distinct morphological variations across the three different lines of sight. Our analysis reveals that speeds derived directly from two-dimensional (2D) images differ significantly from those obtained via three-dimensional (3D) reconstruction. Specifically, the 2D projected speeds measured from STEREO-Ahead, STEREO-Behind, and SOHO are 445, 476, and 336 km s⁻¹, respectively. Furthermore, while 2D measurements suggest a constant propagation speed, the 3D reconstruction reveals a pronounced deceleration of approximately −36 m s⁻². Significant discrepancies are also noted in the deflection amplitude between the 2D and 3D results. Since the propagating streamer deflection effectively traces the shock’s movement, we propose that measuring the deflection speed offers a robust alternative for deriving actual shock velocities in the outer corona, where direct white-light detection remains challenging. Full article

This paper presents an immersive teleoperation framework for service robots that combines real-time 3D human pose estimation with a Virtual Reality (VR) interface to support intuitive, natural robot control. The operator is tracked using MediaPipe for 2D landmark detection and an Intel RealSense D455 RGB-D (Red-Green-Blue plus Depth) camera for depth acquisition, enabling 3D reconstruction of key joints. Joint angles are computed using efficient vector operations and mapped to the kinematic constraints of an anthropomorphic arm on the CHARMIE service robot. A VR-based telepresence interface provides stereoscopic video and head-motion-based view control to improve situational awareness during manipulation tasks. Experiments in real-world object grasping demonstrate reliable arm teleoperation and effective telepresence; however, vision-only estimation remains limited for axial rotations (e.g., elbow and wrist yaw), particularly under occlusions and unfavorable viewpoints. The proposed system provides a practical pathway toward low-cost, sensor-driven, immersive human–robot interaction for service robotics in dynamic environments. Full article

Three-dimensional (3D) printing enables accurate implant pre-shaping in orbital reconstruction but is costly and time-consuming. Naked-eye stereoscopic displays (NEDs) enable virtual implant modeling without fabrication. This study aimed to compare the reproducibility and accuracy of NED-based virtual reality (VR) pre-shaping with conventional 3D-printed models. Two surgeons pre-shaped implants for 11 unilateral orbital floor fractures using both 3D-printed and NED-based VR models with identical computed tomography data. The depth, area, and axis dimensions were measured, and reproducibility and agreement were assessed using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and shape similarity metrics—Hausdorff distance (HD) and root mean square error (RMSE). Intra-rater ICCs were ≥0.80 for all parameters except depth in the VR model. The HD and RMSE reveal no significant differences between 3D (2.64 ± 0.85 mm; 1.02 ± 0.42 mm) and VR (3.14 ± 1.18 mm; 1.24 ± 0.53 mm). Inter-rater ICCs were ≥0.80 for the area and axes in both modalities, while depth remained low. Between modalities, no significant differences were found; HD and RMSE were 2.95 ± 0.94 mm and 1.28 ± 0.49 mm. The NED-based VR pre-shaping achieved reproducibility and dimensional agreement comparable to 3D printing, suggesting a feasible cost- and time-efficient alternative for orbital reconstruction. These preliminary findings suggest that NED-based preshaping may be feasible; however, larger studies are required to confirm whether VR can achieve performance comparable to 3D-printed models. Full article

This study presents an experimental methodology for characterizing the crack-tip region using high-resolution Digital Image Correlation (DIC). The approach utilizes a stereoscopic microscope setup combined with 3D-DIC analysis to enable precise measurements within the small-scale region surrounding the crack tip. Two nonlinear parameters are evaluated: the plastic component of the crack-tip opening displacement (CTOD_p) and the cyclic plastic zone size. The investigation was conducted on disk-shaped compact tension specimens made of AISI 1020 steel under constant-ΔK fatigue testing. The results demonstrate a strong correlation between these nonlinear parameters and fatigue crack propagation, which was maintained stable, validating the proposed methodology. Furthermore, the relevance of crack-tip plasticity in fatigue crack propagation is verified under the tested conditions, highlighting its utility for fatigue life assessment under complex loading scenarios. Full article

The increasing use of immersive 3-D technologies in education raises critical questions about their cognitive impact on learners. This systematic review evaluates how electroencephalography (EEG) and eye-tracking have been used to objectively measure cognitive load in 3-D learning environments. We conducted a comprehensive literature search (2009–2025) across PubMed, Scopus, Web of Science, PsycInfo, and ERIC, identifying 51 studies that used EEG or eye-tracking in experimental contexts involving stereoscopic or head-mounted 3-D technologies. Our findings suggest that 3-D environments may enhance learning and engagement, particularly in spatial tasks, while affecting cognitive load in complex, task-dependent ways. Studies reported mixed patterns across psychophysiological measures, including spectral features (e.g., frontal theta, parietal alpha), workload indices (e.g., theta/alpha ratio), and gaze-based metrics (e.g., fixation duration, pupil dilation): some studies observed increased load, while others reported reductions or no difference. These discrepancies reflect methodological heterogeneity and underscore the value of time-sensitive assessments. While a moderate cognitive load supports learning, an excessive load may impair performance, and overload thresholds can vary across individuals. EEG and eye-tracking offer scalable methods for monitoring cognitive effort dynamically. Overall, 3-D and XR technologies hold promise but must be aligned with task demands and learner profiles and guided by real-time indicators of cognitive load in immersive environments. Full article

The Surface Extraction by TIN-based Search-space Minimization (SETSM) algorithm provides automatic generation of stereo-photogrammetric Digital Surface Models (DSMs) from single stereopairs of stereoscopic images (i.e., stereopairs), eliminating the need for terrain-dependent parameters. SETSM has been extensively validated through the ArcticDEM and Reference Elevation Models for Antarctica (REMA) DSM mapping projects. To enhance DSM coverage, quality, and accuracy by addressing stereopair occlusions, we expand the capabilities of the SETSM algorithm from single stereopair to multiple-pair matching. Building on SETSM’s essential components, we present a SETSM multiple-pair matching procedure (SETSM MMP) that modifies 3D voxel construction, similarity measurement, and blunder detection, among other components. A novel Three-Dimensional Kernel-based Weighted Height Estimation (3D KWHE) algorithm specialized for SETSM accurately determines optimal heights and reduces surface noise. Additionally, an adaptive pixel-to-pixel matching strategy mitigates the effect of differences in ground sample distance (GSD) between images. Validation using space-borne Worldview-2 and air-borne DMC multiple images over urban landscapes, compared to USGS lidar DSM, confirms improved height accuracy and matching success rates. The results from the DMC air-borne images demonstrate efficient elimination of occlusions. SETSM MMP enables high-quality DSM generation in urban environments while retaining the original, single-stereopair SETSM’s high performance. Full article

This study proposes an image processing framework based on bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education. Full article

Background and Objectives: Depth perception of the human visual system in three-dimensional (3D) space plays an important role in human–computer interaction and artificial intelligence (AI) areas. It mainly employs binocular disparity and motion parallax cues. This study aims to systemically summarize the related studies about depth perception specified by these two cues. Materials and Methods: We conducted a literature investigation on related studies and summarized them from aspects like motivations, research trends, mechanisms, and interaction models of depth perception specified by these two cues. Results: Development trends show that depth perception research has gradually evolved from early studies based on a single cue to quantitative studies based on the interaction between these two cues. Mechanisms of these two cues reveal that depth perception specified by the binocular disparity cue is mainly influenced by factors like spatial variation in disparity, viewing distance, the position of visual field (or retinal image) used, and interaction with other cues; whereas that specified by the motion parallax cue is affected by head movement and retinal image motion, interaction with other cues, and the observer’s age. By integrating these two cues, several types of models for depth perception are summarized: the weak fusion (WF) model, the modified weak fusion (MWF) model, the strong fusion (SF) model, and the intrinsic constraint (IC) model. The merits and limitations of each model are analyzed and compared. Conclusions: Based on this review, a clear picture of the study on depth perception specified by binocular disparity and motion parallax cues can be seen. Open research challenges and future directions are presented. In the future, it is necessary to explore methods for easier manipulating of depth cue signals in stereoscopic images and adopting deep learning-related methods to construct models and predict depths, to meet the increasing demand of human–computer interaction in complex 3D scenarios. Full article

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography. Full article

Spatio-temporal measurements of turbulent free surface flows remain challenging with in situ point methods. This study explores the application of an inexpensive depth-sensing RGB-D camera, the Intel^® RealSense™ D455, to capture detailed water surface measurements of a highly turbulent, self-aerated flow in the case of a stepped spillway. Ambient lighting conditions and various sensor settings, including configurations and parameters affecting data capture and quality, were assessed. A free surface profile was extracted from the 3D measurements and compared against phase detection conductivity probe (PDCP) and ultrasonic sensor (USS) measurements. Measurements in the non-aerated region were influenced by water transparency and a lack of detectable surface features, with flow depths consistently smaller than USS measurements (up to 32.5% less). Measurements in the clear water region also resulted in a “no data” region with holes in the depth map due to shiny reflections. In the aerated flow region, the camera effectively detected the dynamic water surface, with mean surface profiles close to characteristic depths measured with PDCP and within one standard deviation of the mean USS flow depths. The flow depths were within 10% of the USS depths and corresponded to depths with 80–90% air concentration levels obtained with the PDCP. Additionally, the depth camera successfully captured temporal fluctuations, allowing for the calculation of time-averaged entrapped air concentration profiles and dimensionless interface frequency distributions. This facilitated a direct comparison with PDCP and USS sensors, demonstrating that this camera sensor is a practical and cost-effective option for detecting free surfaces of high velocity, aerated, and dynamic flows in a stepped chute. Full article

Visuo-spatial attention acts as a filter for the flood of visual information. Until recently, experimental research in this area focused on neural dynamics of shifting attention in 2D space, leaving attentional shifts in depth less explored. In this study, twenty-three participants were cued to attend to one of two overlapping random-dot kinematograms (RDKs) in different stereoscopic depths in a novel experimental setup. These RDKs flickered at two different frequencies to evoke Steady-State Visual Evoked Potentials (SSVEPs), a neural signature of early visual stimulus processing. Subjects were instructed to detect coherent motion events in the to-be-attended-to plane/RDK. Behavioral data showed that subjects were able to perform the task and selectively respond to events at the cued depth. Event-Related Potentials (ERPs) elicited by these events—namely the Selection Negativity (SN) and the P3b—showed greater amplitudes for coherent motion events in the to-be-attended-to compared to the to-be-ignored plane/RDK, indicating that attention was shifted accordingly. Although our new experimental setting reliably evoked SSVEPs, SSVEP amplitude time courses did not differ between the to-be-attended-to and to-be-ignored stimuli. These results suggest that early visual areas may not optimally represent depth-selective attention, which might rely more on higher processing stages, as suggested by the ERP results. Full article

Accurately predicting the future trajectory of road users around autonomous vehicles is crucial for path planning and collision avoidance. In recent years, data-driven vehicle trajectory prediction models have become a significant research focus, and various spatial–temporal neural network models, based on spatial–temporal data, have been proposed. However, some existing spatial–temporal models segregate time and space, neglecting the inherent coupling of time and space. To address this issue, an end-to-end spatial–temporal feature fusion model, based on the Vision Transformer (Vit), is proposed in this paper, which can couple stereoscopic features of diverse spatial regions and time periods. Specifically, we propose an end-to-end spatiotemporal feature coupling model based on visual Transformer, Vit-Traj, which extracts spatiotemporal features through 2D convolution and uses Vit and SENet to complete feature fusion. Experimental results on the NGSIM and HighD datasets indicate that, compared to State-of-the-Art models, the proposed model exhibits better performance. The root mean squared error (RMSE) is 2.72 m on the NGSIM dataset and 0.86 m on the HighD dataset when the prediction horizon is 5 s. Furthermore, ablation experiments are conducted to evaluate the performance of each module, affirming the efficacy of ViT in modeling spatial–temporal data. Full article

Accurate estimates of forest aboveground biomass (AGB) are necessary for the accurate tracking of forest carbon stock. Gaofen-7 (GF-7) is the first civilian sub-meter three-dimensional (3D) mapping satellite from China. It is equipped with a laser altimeter system and a dual-line array stereoscopic mapping camera, which enables it to synchronously generate full-waveform LiDAR data and stereoscopic images. The bulk of existing research has examined how accurate GF-7 is for topographic measurements of bare land or canopy height. The measurement of forest aboveground biomass has not received as much attention as it deserves. This study aimed to assess the GF-7 stereo imaging capability, displayed as topographic features for aboveground biomass estimation in forests. The aboveground biomass model was constructed using the random forest machine learning technique, which was accomplished by combining the use of in situ field measurements, pairs of GF-7 stereo images, and the corresponding generated canopy height model (CHM). Findings showed that the biomass estimation model had an accuracy of R² = 0.76, RMSE = 7.94 t/ha, which was better than the inclusion of forest canopy height (R² = 0.30, RMSE = 21.02 t/ha). These results show that GF-7 has considerable application potential in gathering large-scale high-precision forest aboveground biomass using a restricted amount of field data. Full article

This paper proposes an efficient encoding framework based on Scalable High Efficiency Video Coding (SHVC) technology, which supports both low- and high-resolution 2D videos as well as stereo 3D (S3D) video simultaneously. Previous studies have introduced Cross-View SHVC, which encodes two videos with different viewpoints and resolutions using a Cross-View SHVC encoder, where the low-resolution video is encoded as the base layer and the other video as the enhancement layer. This encoder provides resolution diversity and allows the decoder to combine the two videos, enabling 3D video services. Even when 3D videos are composed of left and right videos with different resolutions, the viewer tends to perceive the quality based on the higher-resolution video due to the binocular suppression effect, where the brain prioritizes the high-quality image and suppresses the lower-quality one. However, recent experiments have shown that when the disparity between resolutions exceeds a certain threshold, it can lead to a subjective degradation of the perceived 3D video quality. To address this issue, a conditional replenishment algorithm has been studied, which replaces some blocks of the video using a disparity-compensated left-view image based on rate–distortion cost. This conditional replenishment algorithm (also known as VEI technology) effectively reduces the quality difference between the base layer and enhancement layer videos. However, the algorithm alone cannot fully compensate for the quality difference between the left and right videos. In this paper, we propose a novel encoding framework to solve the asymmetry issue between the left and right videos in 3D video services and achieve symmetrical video quality. The proposed framework focuses on improving the quality of the right-view video by combining the conditional replenishment algorithm with Cross-View SHVC. Specifically, the framework leverages the non-HEVC option of the SHVC encoder, using a VEI (Video Enhancement Information) restored image as the base layer to provide higher-quality prediction signals and reduce encoding complexity. Experimental results using animation and live-action UHD sequences show that the proposed method achieves BD-RATE reductions of 57.78% and 45.10% compared with HEVC and SHVC codecs, respectively. Full article