Stereoscopic Research Articles

Effect of fluid motions on finite spheres released in turbulent boundary layers

This paper extends the work in Tee et al. (Intl J. Multiphase Flow, vol. 133, 2020, 103462) to investigate the effect of turbulent fluid motions on the translation and rotation of lifting and wall-interacting spheres in boundary layers. Each sphere was released from rest in smooth-wall boundary layers with $Re_\tau =670$ and 1300 ( $d^+=56$ and 116, respectively) and allowed to propagate with the incoming fluid. Sphere and surrounding fluid motions were tracked simultaneously via three-dimensional particle tracking velocimetry and stereoscopic particle image velocimetry in streamwise–spanwise planes. Two-point correlations of sphere and fluid streamwise velocities yielded long positive regions associated with long fast- and slow-moving zones that approach and move over the spheres. The related spanwise correlations were shorter due to the shorter coherence length of spanwise fluid structures. In general, spheres lag the surrounding fluid. The less-dense lifting sphere had smaller particle Reynolds numbers varying from near zero up to 300. Its lift-offs coincided with oncoming fast-moving zones and fluid upwash. Wall friction initially retarded the acceleration of the denser sphere. Later, fluid torque associated with approaching high-velocity regions initiated forward rotation. The rotation, which was long-lived, induced sufficient Magnus lift to initiate repeated small lift-offs, reduce wall friction, and accelerate the sphere to higher sustained velocity. Particle Reynolds numbers remained above 200, and vortex shedding was omnipresent such that the spheres clearly altered the fluid motion. Spanwise fluid shear occasionally initiated wall-normal sphere rotation and relatively long-lasting Magnus side lift. Hence the finite sphere size contributed to multiple dynamical effects not present in point-particle models.

Quality assessment of stereoscopic images under acousto-optic diffraction in paratellurite crystal

Quality assessment of stereoscopic images under acousto-optic diffraction in paratellurite crystal

Calibrating an airborne linear-array multi-camera system on the master focal plane with existing bundled images

ABSTRACT Integrating multiple hyperspectral linear-array cameras into a Linear-array Multi-camera System (LMS) enables an expanded Field of View (FOV) while preserving high spatial resolution. However, the constrained capabilities of tie points are weakened by the limited overlap and short baseline between sub-cameras, hindering the attainment of reliable solutions for the theoretically ideal relative calibration model. Additionally, the absence of stereoscopic imaging in the LMS introduces the challenge of acquiring dense Ground Control Points (GCPs) throughout the FOV during calibration. In this paper, for calibrating an advanced airborne hyperspectral LMS named Arial Hyperspectral System-1000 (AHS-1000), we propose an equivalent geometric model that transforms the three-camera configuration of the LMS into a single-camera representation on the master camera’s focal plane, which was successfully applied to the data processing system of AHS-1000. We extracted dense GCPs by matching each Level-1 sub-image of the LMS with the bundled images of an existing three-line-scanner aerial image dataset, whose orientation parameters have been accurately solved by bundle adjustment with sparse GCPs; the matched points of each sub-image were consistently projected onto the master focal plane as image observations for our equivalent model. Finally, we established a collinearity equation system and performed a combined strip adjustment to calibrate the distortion terms for each pixel of the equivalent camera. The results demonstrate the effectiveness and robustness of our equivalent modeling method, which outperforms the classic modeling method by 2.83 pixels in interior accuracy and 0.86 pixels in relative accuracy, as well as outperforming another similar equivalent modeling method by 1.22 pixels in interior accuracy and 0.18 pixels in relative accuracy. Additionally, our comparison utilizing bundled images, public DOM (Digital Orthophoto Maps) /DEM (Digital Elevation Models), and high-precision DOM/DEM from aerial three-line-scanner demonstrates that bundled images can achieve higher calibration accuracy.

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above.

Using a novel approach to classification images (CIs), we investigated the visual expertise of surveyors for luminance and binocular disparity cues simultaneously after screening for stereoacuity. Stereoscopic aerial images of hedges and ditches were classified in 10,000 trials by six trained remote sensing surveyors and six novices. Images were heavily masked with luminance and disparity noise simultaneously. Hedge and ditch images had reversed disparity on around half the trials meaning hedges became ditch-like and vice versa. The hedge and ditch images were also flipped vertically on around half the trials, changing the direction of the light source and completing a 2 × 2 × 2 stimulus design. CIs were generated by accumulating the noise textures associated with "hedge" and "ditch" classifications, respectively, and subtracting one from the other. Typical CIs had a central peak with one or two negative side-lobes. We found clear differences in the amplitudes and shapes of perceptual templates across groups and noise-type, with experts prioritizing binocular disparity and using this more effectively. Contrariwise, novices used luminance cues more than experts meaning that task motivation alone could not explain group differences. Asymmetries in the luminance CIs revealed individual differences for lighting interpretation, with experts less prone to assume lighting from above, consistent with their training on aerial images of UK scenes lit by a southerly sun. Our results show that (i) dual noise in images can be used to produce simultaneous CI pairs, (ii) expertise for disparity cues does not depend on stereoacuity, (iii) CIs reveal the visual strategies developed by experts, (iv) top-down perceptual biases can be overcome with long-term learning effects, and (v) CIs have practical potential for directing visual training.

LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.

With the widespread adoption of modern RGB cameras, an abundance of RGB images is available everywhere. Therefore, multi-view stereo (MVS) 3D reconstruction has been extensively applied across various fields because of its cost-effectiveness and accessibility, which involves multi-view depth estimation and stereo matching algorithms. However, MVS tasks face noise challenges because of natural multiplicative noise and negative gain in algorithms, which reduce the quality and accuracy of the generated models and depth maps. Traditional MVS methods often struggle with noise, relying on assumptions that do not always hold true under real-world conditions, while deep learning-based MVS approaches tend to suffer from high noise sensitivity. To overcome these challenges, we introduce LNMVSNet, a deep learning network designed to enhance local feature attention and fuse features across different scales, aiming for low-noise, high-precision MVS 3D reconstruction. Through extensive evaluation of multiple benchmark datasets, LNMVSNet has demonstrated its superior performance, showcasing its ability to improve reconstruction accuracy and completeness, especially in the recovery of fine details and clear feature delineation. This advancement brings hope for the widespread application of MVS, ranging from precise industrial part inspection to the creation of immersive virtual environments.

Beach surface model construction: A strategy approach with structure from motion - multi-view stereo

In contrast to traditional beach profiling methods like topographic surveys and GNSS, which pose significant challenges in terms of cost and time, this research underscores the efficiency, cost-effectiveness, and simplicity of terrestrial photogrammetry employing the Structure from Motion-Multi View Stereo (SfM-MVS) method. Notably, this approach enables the utilization of commonplace devices such as smartphones for data capture. The methodology integrates a 12-megapixel camera for image acquisition, processed through Agisoft Metashape Professional software, and validated for accuracy using ground control points (GCPs) and checkpoints (CKPs) calibrated via GNSS. Findings reveal substantial disparities in positional accuracy according to the Ground Control Points distribution. The study underscores the critical role of strategically distributing GCPs and CKPs in effectively mapping coastal areas, thus affirming the potential of SfM-MVS as a powerful and accessible tool for coastal monitoring initiatives. This research contributes significantly to advancing the efficiency and accessibility of beach profile monitoring, offering invaluable insights for researchers and practitioners in coastal management and environmental conservation efforts.•A simplified beach profile modeling methodology is proposed.•The method is faster and more cost-effective than traditional surveys (RTK GNSS, lidar, RPA).•The study highlights the importance of GCP and CKP distribution in enhancing SfM-MVS accuracy for coastal mapping.

Characteristics of beta parapapillary atrophy in primary angle-closure suspect

ObjectiveTo investigate the characteristics of beta parapapillary atrophy (β-PPA) in patients with primary angle-closure suspect (PACS).Methods and analysisIn total, 215 and 259 eyes with PACS and non-PACS (NPACS), respectively, were...

Statistical analysis on branching characteristics of positive streamer discharges in N2–O2 mixtures

Streamers are fast-propagating ionization channels that can usually branch and form complex tree-like structures in dielectric media. In this paper, we perform experiments on positive streamers in different N2–O2 mixtures under varying conditions including voltage, pressure, and electrode geometry, with at least 125 discharge images captured for each condition. We present a statistical analysis on streamer branching characteristics from 3D models that are reconstructed by stereoscopic stroboscopic images and our dedicated semi-automatic 3D reconstruction method.We found that by varying the concentration of O2, the morphology and branching characteristics are greatly changed. Specifically, the average branching angle decrease significantly from 90∘ in air to 66∘ in 1% O2, suggesting that photoionization plays an important role in streamer branching. The branching angles in our work are generally larger than previously reported results due to the resolved 3D structures of discharges by our method. A linear relation between the streamer diameter ratio and the branching direction difference of two daughter branches is found, which intersects the vertical axis almost at unity. It is also found that the average branching angles, streamer velocities and diameters increase as the voltage increases. This is again attributed to stronger photoionization effect under higher voltages. The velocities and diameters are similar at different pressures but at the same reduced electric field. The average branching angle decreases from 90∘ at 133 mbar to 79∘ at 200 mbar. This suggests that stochastic fluctuations become dominant over photoionization effect at higher pressures.

Multi-view Deep Learning for Imaging Atmospheric Cherenkov Telescopes

This research note concerns the application of deep-learning-based multi-view-imaging techniques to data from the High Energy Stereoscopic System Imaging Atmospheric Cherenkov Telescope array. We find that the earlier the fusion of layer information from different views takes place in the neural network, the better our model performs with this data. Our analysis shows that the point in the network where the information from the different views is combined is far more important for the model performance than the method used to combine the information.

Nonlinearly Optimized Dual Stereo Visual Odometry Fusion

Visual odometry (VO) is an important problem studied in robotics and computer vision in which the relative camera motion is computed through visual information. In this work, we propose to reduce the error accumulation of a dual stereo VO system (4 cameras) computing 6 degrees of freedom poses by fusing two independent stereo odometry with a nonlinear optimization. Our approach computes two stereo odometries employing the LIBVISO2 algorithm and later merge them by using image correspondences between the stereo pairs and minimizing the reprojection error with graph-based bundle adjustment. Experiments carried out on the KITTI odometry datasets show that our method computes more accurate estimates (measured as the Relative Positioning Error) in comparison to the traditional stereo odometry (stereo bundle adjustment). In addition, the proposed method has a similar or better odometry accuracy compared to ORB-SLAM2 and UCOSLAM algorithms.

High-fidelity 3D reconstruction of plants using Neural Radiance Fields

Accurate reconstruction of plant phenotypes plays a key role in optimizing sustainable farming practices in the field of Precision Agriculture (PA). Currently, optical sensor-based approaches dominate the field, but the need for high-fidelity 3D reconstruction of crops and plants in unstructured agricultural environments remains challenging. Recently, a promising development has emerged in the form of Neural Radiance Fields (NeRF), a novel method that utilizes neural density fields. This technology has shown impressive performance in various novel vision synthesis tasks, but has remained relatively unexplored in the agricultural context. In our study, we focus on two fundamental tasks within plant phenotyping: (1) the synthesis of 2D novel-view images and (2) the 3D reconstruction of crop and plant models. We explore the world of NeRF, in particular two state-of-the-art (SOTA) methods: Instant-NGP, which excels in generating high-quality images with impressive training and inference speed, and Instant-NSR, which improves the reconstructed geometry by incorporating the Signed Distance Function (SDF) during training. In particular, we present a novel plant phenotype dataset comprising real plant images from production environments. This dataset is a first-of-its-kind initiative aimed at comprehensively exploring the advantages and limitations of NeRF in agricultural contexts. Our experimental results show that NeRF demonstrates commendable performance in the synthesis of novel-view images and is able to achieve reconstruction results that are competitive with Reality Capture, a leading commercial software for 3D Multi-View Stereo (MVS)-based reconstruction. Moreover, our study also highlights certain drawbacks of NeRF, including relatively slow training speeds, performance limitations in cases of insufficient sampling, and challenges in obtaining geometry quality in complex setups. In conclusion, NeRF introduces a new paradigm in plant phenotyping, providing a powerful tool capable of generating multiple representations, such as multi-view images, point cloud and mesh, from a single process.

Engineering multifunctional UV-polymerizable imidazolidinyl urea-based methacrylate resin for 3D printing: Synthesis, characterization, and antimicrobial properties

3D photo printing provides immense flexibility in shaping polymeric objects with intricate geometries. However, the limited availability of photoactive resins for 3D printing poses numerous challenges in realizing the desired material properties in terms of accuracy, printing resolution, and the tunability of mechanical and biodegradable characteristics. To encounter this, researchers consistently strive to develop new photoresins with multifunctional capabilities. Herein, we have reported for the first time, an easy and straightforward synthesis of imidazolidinyl urea (IU) based photoactive resins for high-resolution 3D printing. Our study delves into the impact of photoinitiator, viscosity, photoinhibitor, and photoabsorber on the 3D printing process. Moreover, a comprehensive examination of the physicochemical properties, encompassing thermal, mechanical, and biodegradation behavior reveals the tunability of these properties by adjusting the monomer-to-crosslinker ratio. Furthermore, our printed polymers exhibited strong antimicrobial properties against gram-positive bacteria B. subtilis and gram-negative bacteria E. coli while maintaining minimal toxicity towards mouse fibroblast (NIH-3 T3) cells. In summary, our newly developed IU-based resin emerges as a competitive alternative, addressing the growing demand for advanced photoresins with superior high-resolution 3D printing capabilities, particularly in biomedical applications. This important achievement not only expands the possibilities of 3D printing technology but also presents a versatile solution with promising implications for various biomedical needs.

Fungal and bacterial species richness in biodeteriorated seventeenth century Venetian manuscripts

Historical paper documents are susceptible to complex degradation processes, including biodeterioration, which can progressively compromise their aesthetic and structural integrity. This study analyses seventeenth century handwritten historical letters stored at the Correr Museum Library in Venice, Italy, exhibiting pronounced signs of biodeterioration. The techniques used encompassed traditional colony isolation on agar plates and proteomics analyses, employing nanoscale liquid chromatography coupled with high-resolution mass spectrometry (nano-LC–MS). Fluorescence microscopy was used for the first time in the historical paper biodeterioration context to supplement the conventional stereoscopic, optical, and scanning electron microscopic imaging techniques. This method enables the visualisation of microorganisms beyond and beneath the paper’s surface through their natural intrinsic autofluorescence in a non-invasive and non-destructive way. The results demonstrate a diverse, complex, and abundant microbiota composed of coexisting fungal and bacterial species (Ascomycota, Mucoromycota, Basidiomycota, Proteobacteria, and Actinobacteria), along with mite carcasses, insects, parasites, and possibly protists. Furthermore, this study reveals certain species that were not previously documented in the biodeterioration of historical paper, including human pathogens, such as Histoplasma capsulatum, Brucella, Candida albicans, and species of Aspergillus (A. flavus, A. fumigatus, A. oryzae, A. terreus, A. niger) known to cause infections or produce mycotoxins, posing substantial risk to both artefacts and humans.

The use of orthoimagery and stereoscopic aerial imagery to identify muskrat (Ondatra zibethicus) houses

AbstractThe muskrat (Ondatra zibethicus) is considered a ubiquitous inhabitant of wetlands across Canada and the United States, but recent studies indicate that muskrat populations in many parts of North America have experienced substantial declines over the last 40–60 years. Monitoring of muskrat abundance is therefore an important task for wildlife managers, but traditional methods such as house counts conducted during ground‐based surveys can be labor‐intensive and time‐consuming. Poor conditions or a lack of access may limit how much of a wetland can be surveyed. Aerial imagery has previously been used to census a diverse array of wildlife populations but is not yet a common tool for muskrat surveys. To investigate the accuracy of this alternative survey method, we collected aerial imagery from coastal wetlands along the north shore of Lake Ontario during the winter of 2014 for examination in both 2D orthorectified and 3D stereoscopic formats. We compared muskrat house counts obtained from imagery to counts recorded by ground survey crews in the same wetlands during the same winter. We found no significant difference between mean muskrat house counts obtained by ground survey crews and orthoimagery observers. In contrast, stereoscopic imagery observers overestimated mean house counts compared to ground survey crews, which we interpret was due to an increase in false positives. Our results indicate that orthoimagery is a promising tool for assessing muskrat occupancy, provides comparable broad‐scale results to traditional ground survey methods, and may be preferable to wildlife managers for a variety of reasons.

IINet: Implicit Intra-inter Information Fusion for Real-Time Stereo Matching

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due to their remarkable accuracy. However, the high complexity of 3D convolution makes it challenging to strike a balance between accuracy and speed. Notably, explicit 3D volumes contain considerable redundancy. In this study, we delve into more compact 2D implicit network to eliminate redundancy and boost real-time performance. However, simply replacing explicit 3D networks with 2D implicit networks causes issues that can lead to performance degradation, including the loss of structural information, the quality decline of inter-image information, as well as the inaccurate regression caused by low-level features. To address these issues, we first integrate intra-image information to fuse with inter-image information, facilitating propagation guided by structural cues. Subsequently, we introduce the Fast Multi-scale Score Volume (FMSV) and Confidence Based Filtering (CBF) to efficiently acquire accurate multi-scale, noise-free inter-image information. Furthermore, combined with the Residual Context-aware Upsampler (RCU), our Intra-Inter Fusing network is meticulously designed to enhance information transmission on both feature-level and disparity-level, thereby enabling accurate and robust regression. Experimental results affirm the superiority of our network in terms of both speed and accuracy compared to all other fast methods.

Irregular boundaries stereo images dataset creating using depth estimation model

AbstractThis paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.

Deep synthesis and exploration of omnidirectional stereoscopic environments from a single surround-view panoramic image

We introduce an innovative approach to automatically generate and explore immersive stereoscopic indoor environments derived from a single monoscopic panoramic image in an equirectangular format. Once per 360° shot, we estimate the per-pixel depth using a gated deep network architecture. Subsequently, we synthesize a collection of panoramic slices through reprojection and view-synthesis employing deep learning. These slices are distributed around the central viewpoint, with each slice’s projection center placed on the circular path covered by the eyes during a head rotation. Furthermore, each slice encompasses an angular extent sufficient to accommodate the potential gaze directions of both the left and right eye and to provide context for reconstruction. For fast display, a stereoscopic multiple-center-of-projection stereo pair in equirectangular format is composed by suitably blending the precomputed slices. At run-time, the pair is loaded in a lightweight WebXR viewer that responds to head rotations, offering both motion and stereo cues. The approach combines and extends state-of-the-art data-driven techniques, incorporating several innovations. Notably, a gated architecture is introduced for panoramic monocular depth estimation. Leveraging the predicted depth, the same gated architecture is then applied to the re-projection of visible pixels, facilitating the inpainting of occluded and disoccluded regions by incorporating a mixed Generative Adversarial Network (GAN). The resulting system works on a variety of available VR headsets and can serve as a base component for immersive applications. We demonstrate our technology on several indoor scenes from publicly available data.

Binocular reconstruction of breaking ship bow waves in circulating water channel

This study conducts an experiment on the bow waves generated by a 1:80 KRISO container ship (KCS) model in a circulating water channel. The Froude numbers (characterized by the length of perpendiculars) reach high conditions of 0.377, 0.424, and 0.471. A binocular imaging system is employed to reconstruct three-dimensional configurations of the bow wave surface. The dynamic characteristics of bow wave fields are investigated by observing the standard deviation and employing the Proper Orthogonal Decomposition (POD) method. The power spectral density function presents a power-law relationship with frequency. A novel technique referred as Stereoscopic Foam Image Velocimetry (SFIV) is developed to extract the instantaneous three-dimensional velocity of the foam in breaking area. The velocity field obtained through SFIV defines the nominal time-averaged velocity and nominal turbulent energy. A correlation mapping from pixel coordinates to world coordinates is established to evaluate the actual physical uncertainty. The results demonstrate the superior spatiotemporal accuracy and resolution of binocular imaging technology in measuring the characteristics of bow waves. This research provides significant experimental data of surface three-dimensional configurations for the bow waves breaking issues at high Froude numbers.

Intrafractional motion detection for spine SBRT via X-ray imaging using ExacTrac Dynamic

PurposeDue to its close vicinity to critical structures, especially the spinal cord, standards for safety for spine stereotactic body radiotherapy (SBRT) should be high. This study was conducted, to evaluate intrafractional motion during spine SBRT for patients without individualized immobilization (e.g., vacuum cushions) using high accuracy patient monitoring via orthogonal X-ray imaging. MethodsIntrafractional X-ray data were collected from 29 patients receiving 79 fractions of spine SBRT. No individualized immobilization devices were used during the treatment. Intrafractional motion was monitored using the ExacTrac Dynamic (ETD) System (Brainlab AG, Munich, Germany). Deviations were detected in six degrees of freedom (6 DOF). Tolerances for repositioning were 0.7 mm for translational and 0.5° for rotational deviations. Patients were repositioned when the tolerance levels were exceeded. ResultsOut of the 925 pairs of stereoscopic X-ray images examined, 138 (15 %) showed at least one deviation exceeding the predefined tolerance values. In all 6 DOF together, a total of 191 deviations out of tolerance were recorded. The frequency of deviations exceeding the tolerance levels varied among patients but occurred in all but one patient. Deviations out of tolerance could be seen in all 6 DOF. Maximum translational deviations were 2.6 mm, 2.3 mm and 2.8 mm in the lateral, longitudinal and vertical direction. Maximum rotational deviations were 1.8°, 2.6° and 1.6° for pitch, roll and yaw, respectively. Translational deviations were more frequent than rotational ones, and frequency and magnitude of deviations showed an inverse correlation. ConclusionIntrafractional motion detection and patient repositioning during spine SBRT using X-ray imaging via the ETD System can lead to improved safety during the application of high BED in critical locations. When using intrafractional imaging with low thresholds for re-positioning individualized immobilization devices (e.g. vacuum cushions) may be omitted.

ERROR ANALYSIS OF VISUAL ODOMETRY FOR A SMALL SIZE UNMANNED AERIAL VEHICLE

Abstract. Usage of small size Unmanned Aerial Vehicle (UAV) with autonomous flight capability became widespread at both civil and military applications because of its low costs and ease of use. However, inertial or GPS based positioning and orientation sensors can be disabled at battlefields and the UAV losses its path in a short time. Visual odometry becomes a solution for the aforementioned hassle. Real-time detection of ground control points, matching the same control points at the stereo pair images and correcting the position and orientation data with least square adjustment are the challenges of visual odometry. In this study, error analysis of position and orientation of a UAV flying through mountainous region which cannot obtain any augmenting data for position and orientation is conducted by error propagation law. The flight of UAV is simulated with exact initial position and orientation data. Optic images of ground obtained by nadir directed camera are georeferenced using the digital elevation model of the region, interior and exterior camera parameters. The digital elevation model is used to correct the relief displacement. The stereo-pair image is obtained when the UAV flies 200 m and image coordinates of the common control points are detected by colinearity equations. Position and orientation of UAV is corrected by least square adjustment and the process is repeated for each obtained image acquired at 200 m interval. The analysis revealed that the precisions of horizontal and vertical positioning becomes 75 and 200 meter, respectively. The precision of roll angle becomes worse than 0.5 degree, while the precisions of pitch and yaw angle are estimate to be better that 0.1 degree.