3D Perspective Research Articles

Spatiotemporal desynchronization in the propagation from meteorological to soil moisture drought in the Loess Plateau, China

Study RegionThe Loess Plateau (LP) of China Study FocusMeteorological drought (MD) would propagate to soil moisture drought (SMD) with spatiotemporal desynchronization. The spatial desynchronization between them has frequently been ignored in previous studies due to limitation of identified droughts, which did not consider their 3-dimensional (3D, i.e. longitude, latitude and time) properties. This study presents a 3D perspective on the spatiotemporal desynchronization in the propagation from meteorological to soil moisture drought in the the Loess Plateau (LP) of China, using an improved drought matching method. Event Synchronization (ES) is extended to determine temporal linkage of the two types of droughts and spatial connection is tested using overlapping area. New Hydrological Insights for the RegionThe results showed that: (1) the improved method is reasonable for identifying MDs that trigger SMDs, down to specific clusters; (2) 8 SMDs preceded MDs 1 month, while approximately 79 % of SMDs did not recover after the determination of MDs; (3) severity of MD is an impact factor on recovery lag, while antecedent soil moisture dominates onset lag with the relative importance of approximately 50 %; and (4) incompletely overlap in migration trajectory between the two types of droughts was mainly caused by temperature, followed by antecedent soil moisture and potential evapotranspiration, with relative importance of 55 %, 14 % and 12 %, respectively.

3D Head Pose Estimation via Normal Maps: A Generalized Solution for Depth Image, Point Cloud, and Mesh

Head pose estimation plays a crucial role in various applications, including human–machine interaction, autonomous driving systems, and 3D reconstruction. Current methods address the problem primarily from a 2D perspective, which limits the efficient utilization of 3D information. Herein, a novel approach, called pose orientation‐aware network (POANet), which leverages normal maps for orientation information embedding, providing abundant and robust head pose information, is introduced. POANet incorporates the axial signal perception module and the rotation matrix perception module, these lightweight modules make the approach achieve state‐of‐the‐art (SOTA) performance with few computational costs. This method can directly analyze various topological 3D data without extensive preprocessing. For depth images, POANet outperforms existing methods on the Biwi Kinect head pose dataset, reducing the mean absolute error (MAE) by ≈30% compared to the SOTA methods. POANet is the first method to perform rigid head registration in a landmark‐free manner. It also incorporates few‐shot learning capabilities and achieves an MAE of about on the Headspace dataset. These features make POANet a superior alternative to traditional generalized Procrustes analysis for mesh data processing, offering enhanced convenience for human phenotype studies.

AI-assisted 3D analysis of grasping and reaching behavior of squirrel monkeys during recovery from cervical spinal cord injury

We have previously demonstrated that machine learning-based video analysis, conducted via DeepLabCut, is more sensitive for detecting subtle deficits in hand grasping behavior than traditional end-point performance assessments. This superiority was observed in a nonhuman primate (NHP) model of cervical spinal cord injury, specifically a dorsal column lesion (DCL). The current study aims to further characterize the kinematic aspects of the deficits in hand reaching, grasping, and retrieving behavior from a 3D perspective following a DCL. Squirrel monkeys were trained to retrieve sugar pellets from eight wells, which were located either on a flat plate or a raised tube with varying well depths. This setup was designed to require coordinated finger movements during the task. Immediately after the DCL, the animals exhibited measurable behavioral deficits. These were characterized by significant increases in grasping speed squared and trial completion time, markedly widened movement trajectories of individual fingers, and abnormalities in inter-finger distance and orientation. Increased task difficulty was associated with more pronounced behavioral deficits. By three months post-DCL, video-based measurements indicated no significant recovery, even though global end-point performance had returned to baseline levels. Our findings demonstrate that deprivation of tactile information results in impaired dexterous hand behavior involving coordinated finger movements, and the impairment is sustained for 20 weeks. This spinal cord injury (SCI) model, along with DeepLapCut analysis, provides a valuable platform for separately evaluating sensory and motor functions and their contributions to dexterous hand behavior and may be used for evaluating therapeutic interventions using more sensitive behavioral outcome readouts.

Geometry-semantic aware for monocular 3D Semantic Scene Completion

Monocular Semantic Scene Completion (SSC) empowers intelligent devices to comprehend voxel occupancy (geometry) and semantics in 3D scenes, attracting significant attention in indoor and autonomous driving scenarios. However, existing monocular SSC models primarily map 2D images into 3D space, neglecting the potential benefits of leveraging semantic and geometric understanding in 2D. To address this, we propose the Proxy-embedding Parallel Multi-task Network (PPMNet), which aims to perceive the geometry and semantics of 3D space through depth estimation and semantic segmentation proxy tasks on the 2D perspective plane. Moreover, 2D plane features can be inversely projected into 3D space and subsequently processed using the 3D network. In addition, we enhance contextual awareness in both perspective planes and voxel grids through parallel 2D and 3D decoders. Furthermore, we employ Dual-Head Pyramid Pooling (DHPP) to aggregate information from these two representations. Finally, considering the class imbalance and label incompleteness in practical data, we design a local-to-global loss to prioritize challenging categories. Extensive experiments validate our superiority over state-of-the-art methods on the NYUv2 and SemanticKITTI datasets. The code is available at: https://github.com/luzonghao1/PPMNet.

Exploring swine oviduct anatomy through micro-computed tomography: a 3D modeling perspective.

The oviduct plays a crucial role in the reproductive process, serving as the stage for fertilization and the early stages of embryonic development. When the environment of this organ has been mimicked, it has been shown to enhance in vitro embryo epigenetic reprogramming and to improve the yield of the system. This study explores the anatomical intricacies of two oviduct regions, the uterotubal junction (UTJ) and the ampullary-isthmic junction (AIJ) by using micro-computed tomography (MicroCT). In this study, we have characterized and 3D-reconstructed the oviduct structure, by measuring height and width of the oviduct's folds, along with the assessments of fractal dimension, lacunarity and shape factor. Results indicate distinct structural features in UTJ and AIJ, with UTJ displaying small, uniformly distributed folds and high lacunarity, while AIJ shows larger folds with lower lacunarity. Fractal dimension analysis reveals values for UTJ within 1.189-1.1779, while AIJ values range from 1.559-1.770, indicating differences in structural complexity between these regions. Additionally, blind sacs or crypts are observed, akin to those found in various species, suggesting potential roles in sperm sequestration or reservoir formation. These morphological differences align with functional variations and are essential for developing an accurate 3D model. In conclusion, this research provides information about the oviduct anatomy, leveraging MicroCT technology for detailed 3D reconstructions, which can significantly contribute to the understanding of geometric-morphological characteristics influencing functional traits, providing a foundation for a biomimetic oviduct-on-a-chip.

Lighting Projection Design of Art Stage From Holographic Naked Eye 3D Perspective

In today's modern environment, as an important part of stage art and performance, many places need to use stage lighting. Therefore, the correct use of stage lighting design is particularly important, and it is necessary to study the design of stage lighting. However, the traditional stage light projection is designed by the designer according to the understanding of music and music matching light projection. This scheme is easily influenced by the designer's subjective factors, which lack a clear and perfect design scheme. In this context, from the perspective of holographic naked eye 3D, this paper designs from the stage layout, utilization rate of lighting, the analysis of music features and the distribution of different colors, and evaluates the merits and disadvantages of music and light shows. This evaluation method can provide a design basis for the light projection in the design process of art stage and provide a scientific basis for the evaluation method of performance scheme. It can greatly reduce the design and debugging time of the scheme and save the design cost of the lighting projection.

Morphometric evaluation of the anatomical relationships between the superior orbital fissure and the orbital structures based on computed tomography images with clinical implications.

To assist in surgical planning in endoscopic approaches, we analyzed the morphometric measurements of the superior orbital fissure (SOF) and optic canal (OC) by three-dimensional multislice computed tomography (3D MDCT) and evaluated them according to age, gender, and lateralization. The study analyzed 219 MDCT images (114 women, 105 men) from individuals aged 18-90. Measurements of SOF and OC were performed on 3D MDCT images in the axial plane and with 3D-Slicer software. The distance between the infraorbital foramen and the anterior entrance of the maxillary sinus (CBW) (p < 0.001), the distance between the CBW and the lateral point of the SOF (p = 0.001), and the Angle 1 (p = 0.028) were higher in women than in men. While the SOF length and on 3D the SOF width were higher in women than men (p < 0.001 and (p = 0.001, respectively), the lateral wall length OC was higher in men than women (p = 0.045). According to SOF classification, SOF length was highest in type II and lowest in type VIII (p = 0.025), SOF width was highest in type I and lowest in type VI (p < 0.001). No significant difference was found based on age groups and lateralization in all parameters. We found that as the SOF width increased, the SOF length also increased, and there was a statistically strong positive correlation. These findings can contribute to a more effective and safe operation by improving and updating surgeons' knowledge about safe distances to SOF in endoscopic procedures from a 3D MDCT perspective.

Crowd Scene Anomaly Detection in Online Videos

Abstract. The prevalence of surveillance cameras in public places has led to an extremely pressing need for effective position and crowd monitoring, as well as anomaly detection. This paper tends to exhibit an incorporated approach that combines state-of-the-art computer vision techniques for comprehensive crowd surveillance. The main features of our approach are summarized into four steps: (a) Object detection and tracking; (b) Geometric rectification for positioning; (c) Motion extraction; and (d) Anomaly detection. First, this uses YOLOv5's Convolutional Neural Network (CNN) model in making efficient detection of objects, focusing on spotting individuals within crowded scenes. After detection, a strong mechanism for tracking is established with the help of the DeepSORT algorithm, which can track the person across frames. It must gain the people's position in the video frame and analyze motion data with the guarantee of capture of camera-scene geometry. Each frame thus gets converted from the 3D perspective to a 2D bird's eye view within the surveillance video, giving a guarantee of capture of the geometry of a camera scene. Motion anomaly detection is addressed through statistical methods, with Kernel Density Estimation (KDE) being employed to identify deviations from normal motion patterns. Extensive experiments conducted on different online crowd scene video datasets validate the effectiveness of the proposed anomaly detection mechanism. Overall, this integrated approach proposes a promising solution to crowd surveillance, further development of object detection, tracking, and anomaly analysis for monitoring public spaces.

A complementary approach for neocortical cytoarchitecture inspection with cellular resolution imaging at whole brain scale

Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain’s 3D anatomical structure.

Error correction method based on dual-beam laser for curved rail profile measurement

The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane’s normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

Advanced Fire Investigation Technique Utilizing Virtual Reality

This paper presents an advanced technique for fire investigation via the development and application of a virtual reality (VR)-based system. After capturing a fire scene with a 360° camera, the acquired image files are processed, edited, and executed through a developed program to reconstruct the scene in VR. Currently, fire investigation methods involve writing reports based on photographed pictures. However, this 2D approach has limitations, as individuals who cannot visit the site in person lack a sense of presence and immersion. When the proposed technique is applied to scene investigations, wearing an head-mounted display allows for the investigation of scenes from a 360° 3D perspective that maximizes vivacity and immersion. This technology facilitates collaboration with experts from various fields who could not otherwise participate in a scene, thereby enabling the reliable derivation of fire causes. Additionally, the proposed method allows for the 3D digital preservation of major national disaster incidents and can therefore convey lessons from accidents to future generations. In the future, the proposed VR reconstruction system could be utilized as valid evidence in court cases.

Research on 3D Human Motion Visualization for Immersive Teaching Mechanism - 3D Pose Reconstruction form the Tradition Motion Video for Teaching and Evaluation

Objective: Based on the three-dimensional (3D) visualization technology of human motion, the immersive teaching mechanism of human motion was proposed. At present, the training and learning of human motion could not observe the 3D information of its posture due to the issue of two-dimensional (2D) perspective, which had many shortcomings. Methods: This article proposed new teaching mechanisms, including personal immersive assisted instruction, multi person immersive interactive teaching, and immersive teaching evaluation method for motion learning. The design of the three methods were carried out in the background of artificial intelligence (AI). Results: Combining with current advanced AI algorithms, the concept of visualizing 3D human motion for immersive teaching method was conceived, and a specific implementation framework diagram and description of the main work of each teaching mechanism are provided. The personal immersive assisted instruction method would be a powerful tool for enhancing individual learning experiences in the realm of 3D human motion visualization. The multi-person immersive interactive teaching took 3D human motion visualization to the next level by facilitating collaborative learning experiences. The immersive teaching evaluation method for human motion training was pivotal for assessing the effectiveness of these immersive techniques. Conclusion: After the evaluation and analysis, this mechanism had been proven to be an effective teaching mechanism for pose learning. 3D human motion visualization for the immersive teaching held the promising prospects in the field of education and instructional practices.

Spike structure of gold nanobranches induces hepatotoxicity in mouse hepatocyte organoid models

BackgroundGold nanoparticles (GNPs) have been extensively recognized as an active candidate for a large variety of biomedical applications. However, the clinical conversion of specific types of GNPs has been hindered due to their potential liver toxicity. The origin of their hepatotoxicity and the underlying key factors are still ambiguous. Because the size, shape, and surfactant of GNPs all affect their properties and cytotoxicity. An effective and sensitive platform that can provide deep insights into the cause of GNPs’ hepatotoxicity in vitro is therefore highly desired.MethodsHere, hepatocyte organoid models (Hep-orgs) were constructed to evaluate the shape-dependent hepatotoxicity of GNPs. Two types of GNPs with different nanomorphology, gold nanospheres (GNSs) and spiny gold nanobranches (GNBs), were synthesized as the representative samples. Their shape-dependent effects on mice Hep-orgs’ morphology, cellular cytoskeletal structure, mitochondrial structure, oxidative stress, and metabolism were carefully investigated.ResultsThe results showed that GNBs with higher spikiness and tip curvature exhibited more significant cytotoxicity compared to the rounded GNSs. The spike structure of GNBs leads to a mitochondrial damage, oxidative stress, and metabolic disorder in Hep-orgs. Meanwhile, similar trends can be observed in HepG2 cells and mice models, demonstrating the reliability of the Hep-orgs.ConclusionsHep-orgs can serve as an effective platform for exploring the interactions between GNPs and liver cells in a 3D perspective, filling the gap between 2D cell models and animal models. This work further revealed that organoids can be used as an indispensable tool to rapidly screen and explore the toxic mechanism of nanomaterials before considering their biomedical functionalities.

Designing stepping-stones landscapes: a 2D perspective does not lead to more standardization than an in-situ perspective.

Previous research found that when participants across the lifespan could be the architect of their own stepping-stones landscapes, they create nonstandardized configurations with gap-width variation. Yet, architects often use standardized dimensions in their designs for playgrounds and outdoor fitness areas. To scrutinize why architects tend to seek for more standardized designs than the examined target users, we tested the hypothesis that the difference is caused by a different perspective during the making process. After all, landscape architects generally design on 2D maps, while the participants designed in situ. We asked 67 participants to design a stepping-stones landscape on a 2D map and 67 other participants to create the landscape in situ. Contrary to our expectations, we found no indications that designing on a 2D map leads to more standardized configurations. We end with discussing other characteristics of the design processes that could potentially explain the omnipresent standardization in design.

Estimating urban land subsidence with satellite data using a spatially multiscale geographically weighted regression approach

Land subsidence has attracted widespread attention because of its potential hazards to sustainable urban development. The traditional methods for estimating land subsidence disregard the crucial aspect of altitude by analyzing spatial data solely on the 2D plane (longitude and latitude geographic coordinates). This research incorporates the altitude feature into the construction of the spatial matrix to develop a new method named spatially multiscale geographically weighted regression (SM-GWR) for a better estimation of land subsidence, enabling to determine the optimal bandwidth at 3D spatial scales. By adopting a 3D perspective, the new algorithm accurately fits settlement points based on spatial distances, addressing previous limitations of projecting points on a two-dimensional surface and ignoring height differences, thereby providing a more realistic depiction of spatial relationships. The satellite data is first used to monitor land subsidence through the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). Then, the SM-GWR model is developed to construct the 3D spatial weight matrix and update estimate parameters through back-fitting calibration. The proposed method's feasibility is confirmed through examples of land subsidence in China and Singapore. The case study results demonstrate that: (1) The SM-GWR model shows significant improvement in fitting land subsidence compared to the traditional method (R2 value of 0.908 and 0.934 in predicting annual subsidence and cumulative subsidence, respectively); (2) The contribution of the influencing factors is highly related to the nature properties and urban development of the city. The novelty of this study lies in the development of an SM-GWR model combined with satellite monitoring to achieve high-precision estimation of land subsidence, considering the 3D structure of spatial geographic data.

Molecular Dynamic Simulations of Aqueous Micellar Organometallic Catalysis: Methane Functionalization as a Case Study.

Molecular Dynamics (MD) simulations constitute a powerful tool that provides a 3D perspective of the dynamical behavior of chemical systems. Herein the first MD study of the dynamics of a catalytic organometallic system, in micellar media, is presented. The challenging methane catalytic functionalization into ethyl propionate through a silver-catalyzed process has been targeted as the case study. The intimate nature of the micelles formed with the surfactants sodium dodecylsulfate (SDS) and potassium perfluorooctane sulfonate (PFOS) has been ascertained, as well as the relative distribution of the main actors in this transformation, namely methane, the diazo reagent and the silver catalyst, the latter in two different forms: the initial compound and a silver-carbene intermediate. Catalyst deactivation occurs with halide containing surfactants dodecyltrimethylammonium chloride (DTAC) and Triton X-100. Computed simulations allow explaining the experimental results, indicating that micelles behave differently regarding the degree of accumulation and the local distribution of the reactants and their effect in the molecular collisions leading to net reaction.

Molecular Dynamic Simulations of Aqueous Micellar Organometallic Catalysis: Methane Functionalization as a Case Study

AbstractMolecular Dynamics (MD) simulations constitute a powerful tool that provides a 3D perspective of the dynamical behavior of chemical systems. Herein the first MD study of the dynamics of a catalytic organometallic system, in micellar media, is presented. The challenging methane catalytic functionalization into ethyl propionate through a silver‐catalyzed process has been targeted as the case study. The intimate nature of the micelles formed with the surfactants sodium dodecylsulfate (SDS) and potassium perfluorooctane sulfonate (PFOS) has been ascertained, as well as the relative distribution of the main actors in this transformation, namely methane, the diazo reagent and the silver catalyst, the latter in two different forms: the initial compound and a silver‐carbene intermediate. Catalyst deactivation occurs with halide containing surfactants dodecyltrimethylammonium chloride (DTAC) and Triton X‐100. Computed simulations allow explaining the experimental results, indicating that micelles behave differently regarding the degree of accumulation and the local distribution of the reactants and their effect in the molecular collisions leading to net reaction.

Surface—subsurface grain structure relationships

Relationships between surface and subsurface grain structure are studied using a large three-dimensional (3D) microstructural dataset of a nickel-base superalloy Inconel 718. Comparative analyses of microstructural features were performed from both an orthographic, two-dimensional (2D) perspective and a full-3D one to underscore their distinctions on a grain-by-grain basis for a given surface microstructure. A positive correlation between grain size and the number of nearest neighbors of surface grains was observed; however, considerable variability exists per grain. We also present a detailed investigation of 3D surface grain structure and highlight the limitations of 2D surface grain characterizations. Several features of subsurface grain structure, including out-of-plane grain boundary inclination angle, surface-to-subsurface feature proximity, and subsurface grain boundary interface geometry are characterized. The analysis reveals a wide array of highly non-columnar subsurface grain structures with many points at the free surface being closer to grain boundaries and triple junctions beneath the surface than in the surface observation plane. Repeating these analyses for a synthetic dataset without twins reveals that our findings apply to other polycrystalline materials with near-equiaxed grains and limited texture.

Photoplethysmography-based cuffless blood pressure estimation: an image encoding and fusion approach

Objective. Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin’s surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective. Approach. In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder. Main results. The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A. Significance. The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement.

Learning Space Transformation

This research aims to develop an interior design plan for the computer laboratory at SMKN 1 North Luwu. The research methodology employed in this study is research and development (R&amp;D). The research process consists of several steps, including analyzing potential issues and challenges, accomplished through an extensive review of existing literature and on-site field studies. Data is collected through methods, including observations, interviews, and documentation. The content of the design development encompasses several crucial aspects. First, it involves the creation of a design concept for the laboratory, which is then translated into detailed 2D working drawings and 3D perspective drawings. These design proposals are rigorously assessed and validated by experts and practitioners. The outcomes of this research effort include a comprehensive interior design plan tailored specifically for the computer laboratory at SMKN 1 North Luwu. This design plan is presented through detailed 2D drawings and 3D modeling using V-ray SketchUp 2020. The validation process of the design resulted in a score of 141 with a percentage of 97.9%, indicating that the product design is highly suitable as an exemplary model for managing the computer laboratory at SMKN 1 North Luwu, with a classification of "excellent."