Three-dimensional Objects Research Articles

Relating visual and pictorial space: Integration of binocular disparity and motion parallax.

Traditionally, perceptual spaces are defined by the medium through which the visual environment is conveyed (e.g., in a physical environment, through a picture, or on a screen). This approach overlooks the distinct contributions of different types of visual information, such as binocular disparity and motion parallax, that transform different visual environments to yield different perceptual spaces. The current study proposes a new approach to describe different perceptual spaces based on different visual information. A geometrical model was developed to delineate the transformations imposed by binocular disparity and motion parallax, including (a) a relief depth scaling along the observer's line of sight and (b) pictorial distortions that rotate the entire perceptual space, as well as the invariant properties after these transformations, including distance, three-dimensional shape, and allocentric direction. The model was fitted to the behavioral results from two experiments, wherein the participants rotated a human figure to point at different targets in virtual reality. The pointer was displayed on a virtual frame that could differentially manipulate the availability of binocular disparity and motion parallax. The model fitted the behavioral results well, and model comparisons validated the relief scaling in the form of depth expansion and the pictorial distortions in the form of an isotropic rotation. Fitted parameters showed that binocular disparity renders distance invariant but also introduces relief depth expansion to three-dimensional objects, whereas motion parallax keeps allocentric direction invariant. We discuss the implications of the mediating effects of binocular disparity and motion parallax when connecting different perceptual spaces.

CaLiJD: Camera and LiDAR Joint Contender for 3D Object Detection

Three-dimensional object detection has been a key area of research in recent years because of its rich spatial information and superior performance in addressing occlusion issues. However, the performance of 3D object detection still lags significantly behind that of 2D object detection, owing to challenges such as difficulties in feature extraction and a lack of texture information. To address this issue, this study proposes a 3D object detection network, CaLiJD (Camera and Lidar Joint Contender for 3D object Detection), guided by two-dimensional detection results. CaLiJD creatively integrates advanced channel attention mechanisms with a novel bounding-box filtering method to improve detection accuracy, especially for small and occluded objects. Bounding boxes are detected by the 2D and 3D networks for the same object in the same scene as an associated pair. The detection results that satisfy the criteria are then fed into the fusion layer for training. In this study, a novel fusion network is proposed. It consists of numerous convolutions arranged in both sequential and parallel forms and includes a Grouped Channel Attention Module for extracting interactions among multi-channel information. Moreover, a novel bounding-box filtering mechanism was introduced, incorporating the normalized distance from the object to the radar as a filtering criterion within the process. Experiments were conducted using the KITTI 3D object detection benchmark. The results showed that a substantial improvement in mean Average Precision (mAP) was achieved by CaLiJD compared with the baseline single-modal 3D detection model, with an enhancement of 7.54%. Moreover, the improvement achieved by our method surpasses that of other classical fusion networks by an additional 0.82%. In particular, CaLiJD achieved mAP values of 73.04% and 59.86%, respectively, thus demonstrating state-of-the-art performance for challenging small-object detection tasks such as those involving cyclists and pedestrians.

ESTIMATION OF VEHICLE YAW SPEED USING PHOTOGRAMMETRY OF TIRE TRACKS AT TRAFFIC ACCIDENT SCENES

Background: This article presents a study on the use of photogrammetry to determine the critical yaw radius of automotive vehicles and, consequently, estimate the skidding speed. Photogrammetry is a non-invasive measurement technique that uses images to digitally reconstruct three-dimensional objects and analyse their geometric characteristics. Aim: The study aims to capture and process images of tire tracks from a vehicle in skidding conditions at the scene of a traffic accident to calculate the critical yaw radius and determine the skid speed. Methods: Images were rectified using specialized software and then processed to calculate the critical yaw radius, allowing the skid speed to be determined based on specific mathematical models. Results: The results obtained demonstrate the predictions and accuracy of photogrammetry as an effective tool for vehicle safety analysis and vehicle dynamics studies in skidding conditions. Discussion: This approach not only provides important information for the development of stability control systems, but also contributes to a deeper understanding of the specificities related to vehicle safety in critical driving situations. Conclusions: Photogrammetry proves to be a valuable method in enhancing vehicle safety analysis and understanding vehicle dynamics during skidding incidents.

How Different Training Types and Computer Anxiety Influence Performance and Experiences in Virtual Reality

Virtual reality (VR) can place people in unique environments and facilitate engagement, making it a compelling tool for storytelling and learning. However, experiencing narratives requires immersion, which can be difficult for those who are anxious about technology. Prior research has shown that training new users on how to use VR before they engage in learning tasks housed in VR is critical. The right kind of training and targeted guidance may help people, including those with computer anxiety, better navigate virtual experiences. However, best practices for how training should be administered remain unclear. This study examined how training type (paper, video, and VR) and computer anxiety influenced outcomes using a large sample size (<em>n</em> = 284). We measured performance and self-reported outcomes while participants navigated computer-graphic scenes, manipulated three-dimensional objects, and watched a narrative 360° video. Results showed that participants who received training via video or VR mastered more VR functions than those who received training via paper. Additionally, those who trained directly in VR had less of a negative experience using VR for completing tasks. Furthermore, participants who trained in VR perceived the training as more useful and found the VR tasks to be easier compared to those who received training in paper or video. Finally, those with high levels of computer anxiety, regardless of training, had more negative outcomes than those with low computer anxiety, including having less mastery of VR functions and engagement with the 360° video content, perceiving the training as being less useful, completing tasks with more difficulty, and having more of a negative experience. Our results suggest that keeping the medium the same both during training and doing is ideal. We discuss implications for theories of information processing in VR, as well as implications for scaled engagement with narratives and learning in VR.

Synergia danych fotogrametrycznych uzyskanych na podstawie zdjęć naziemnych oraz UAV

Methods of short-range digital photogrammetry and unmanned aerial vehicle imagining increasingly find more extensive usage in various types of measurements. In recent years, modern photogrammetric technologies have found wide application in recreating 3D models of any objects. The resulting 3D models can be a source of information that may be used for measurement, inventorying and cataloguing, as well as for visualisation purposes. New methods of acquiring spatial data have been developed, such as laser scanning, imaging using unmanned aerial vehicles or digital non-metric cameras. Synergy and the confluence of these data have expanded the existing measurement possibilities, finding application in 3D modelling, inventorying of monuments, or monument conservation. Photogrammetric methods are comparable to direct measurement methods in terms of their accuracy, as well as speed and cost. They have a number of advantages, including a shortened time required for taking a photo, which is greatly convenient when measuring moving objects. Other advantages of photogrammetric methods include the possibility of development and performing measurements in chamber conditions and the lack of restrictions pertaining to repeating the measurement when errors are found or a need arises to supplement them [Kurczyński and Preuss 2000]. One of the possibilities offered by photogrammetry is the ability to create 3D visualizations. The creation of a three-dimensional model facilitates presenting the inventoried object in a realistic, complete, and up-to-date manner. This paper presents an attempt to synergise data for modelling a three-dimensional architectural object based on photos obtained with the Nikon D7500 non-metric camera and with the DJI Mavic Air unmanned aerial vehicle.

A monocular three-dimensional object detection model based on uncertainty-guided depth combination for autonomous driving

Three-Dimensional (3D) object detection is a crucial task for enhancing safety and efficiency in autonomous driving. However, estimating depth from monocular images remains a challenging task. Most existing monocular 3D object detection methods rely on additional auxiliary data sources to compensate for the lack of spatial information in monocular images. Nevertheless, these methods bring substantial computational overhead and time-consuming preprocessing steps. To address this issue, we propose a novel depth estimation method for monocular images that does not rely on any auxiliary information. Leveraging both the texture and geometric cues of detected objects, our method generates two depth estimates for each object based on the extracted Region of Interest (RoI) features: a direct depth estimate and a height-based depth estimate with uncertainty modeling. Our model dynamically assigns weights to these depth estimates based on their respective uncertainties and combines them to obtain the final depth. During the training process, the model assigns higher weights to depth branches with higher uncertainties, as these estimates exhibit greater tolerance to errors. As the combined depth network introduces increased complexity, we utilize Group Normalization (GN) to better capture spatial information in the prediction branch outputs. Furthermore, we leverage the Two-Dimensional (2D) information of objects to predict the residual in the 2D center after downsampling, aiding in the regression of 3D center. On the KITTI benchmark, our model achieves an average precision (AP) of 16.65 % and 23.19 % on 3D and bird's-eye view (BEV) detection for the moderate category, surpassing the state-of-the-art (SOTA) models in each category.

Representation of aggregates from their two-dimensional images for primary particles of different sizes

This study focused on representing the three-dimensional (3D) structure of individual aggregates based on their two-dimensional (2D) images. This starts with the determination of 2D box-counting fractal dimension (Df,BC,2D), uses a previously derived empirical correlation to obtain 3D power law fractal dimension (Df,PL), and then builds the aggregate on the basis of Df,PL by an existing algorithm. Validation of this procedure can be done in forward or backward manner. Forward validation requires the existence of tomographic measurements of Df,PL. It has been conducted on aggregates of large primary particles produced to this purpose in a spray fluidized bed and analyzed by X-ray micro-computed tomography (μ-CT). For the same agglomerates backward validation has also been exercised, starting the representation from 2D projections of the 3D objects and repeating the same procedure on the represented aggregates to see, how accurately the fractal dimensions of the original objects are reproduced. When the primary particles are too small in size to be resolved by X-ray μ-CT, only 2D imaging data by electron microscopy are usually available. Such images have been taken from literature for aggregates composed of submicron particles or nanoparticles and used for aggregate representation in 3D. Subsequently, backward validation of the procedure has been conducted. Both forward validation and backward validation results indicate a high level of consistency between the fractal characteristics and morphological structures of the represented aggregates and those of the original ones. Additionally, this study shows that the method is effective for aggregates of bidisperse and polydisperse particles.

Supervised Contrastive Learning for 3D Cross-Modal Retrieval

Interoperability between different virtual platforms requires the ability to search and transfer digital assets across platforms. Digital assets in virtual platforms are represented in different forms or modalities, such as images, meshes, and point clouds. The cross-modal retrieval of three-dimensional (3D) object representations is challenging due to data representation diversity, making common feature space discovery difficult. Recent studies have been focused on obtaining feature consistency within the same classes and modalities using cross-modal center loss. However, center features are sensitive to hyperparameter variations, making cross-modal center loss susceptible to performance degradation. This paper proposes a new 3D cross-modal retrieval method that uses cross-modal supervised contrastive learning (CSupCon) and the fixed projection head (FPH) strategy. Contrastive learning mitigates the influence of hyperparameters by maximizing feature distinctiveness. The FPH strategy prevents gradient updates in the projection network, enabling the focused training of the backbone networks. The proposed method shows a mean average precision (mAP) increase of 1.17 and 0.14 in 3D cross-modal object retrieval experiments using ModelNet10 and ModelNet40 datasets compared to state-of-the-art (SOTA) methods.

A generalized framework for identification and geometrical feature extraction of circular and polygonal shaped prisms

Abstract This paper introduces a versatile framework crucial for robotic applications such as object manipulation, mobile robot navigation, and pole climbing. It addresses the identification of geometric shapes and dimensions of diverse objects found in varied environments. The proposed method utilizes LiDAR scanning to capture objects from different angles, generating point clouds merged through transformations and superimpositions. After filtering and slicing, intersections are isolated and projected onto a chosen datum plane. The framework employs Non-Linear Least Square fitting via Gauss Newton iterative approach, utilizing pseudo-inverse Jacobian of a hypotrochoid to approximate polygons. The algorithm consecutively fits polygon prisms, determining the best fit with the least norm of error. Results indicate an average least square error of less than 9% for radius fitting and a high f-score for shape identification.

Comprehensive walkability assessment of urban pedestrian environments using big data and deep learning techniques.

Assessing street walkability is a critical agenda in urban planning and multidisciplinary research, as it facilitates public health, community cohesion, and urban sustainability. Existing evaluation systems primarily focus on objective measurements, often neglecting subjective assessments and the diverse walking needs influenced by different urban spatial elements. This study addresses these gaps by constructing a comprehensive evaluation framework that integrates both subjective and objective dimensions, combining three neighbourhood indicators: Macro-Scale Index, Micro-Scale Index, and Street Walking Preferences Index. A normalization weighting method synthesizes these indicators into a comprehensive index. We applied this framework to assess the street environment within Beijing's Fifth Ring Road. The empirical results demonstrate that: (1) The framework reliably reflects the distribution of walkability. (2) The three indicators show both similarities and differences, underscoring the need to consider the distinct roles of community and street-level elements and the interaction between subjective and objective dimensions. (3) In high-density cities with ring-road development patterns, the Macro-Scale Index closely aligns with the Comprehensive Index, demonstrating its accuracy in reflecting walkability. The proposed framework and findings offer new insights for street walkability research and theoretical support for developing more inclusive, sustainable and walkable cities.

From traditional to digital contexts: new characteristics of the public’s spatial perception of urban streets in the age of technology

The constant development of internet technology in the digital age has changed the way people interact with space. To explore whether and what changes have occurred in the public’s spatial perception of the street during the shift from the traditional to the digital age, this paper compares the public’s spatial perception characteristics in the two contexts through a classical theory review and empirical coding analysis with the Xiaolouxiang historic district as an empirical case. The frameworks of public spatial perception characteristics are similar in both contexts, with a focus on the three dimensions of objects, buildings and spaces, and activities. However, compared with that in the traditional context, the public’s spatial perception of the street in the digital context of Xiaolouxiang has undergone new changes in three aspects: from focusing on the three-dimensional experience of space to focusing on the two-dimensional visual aesthetics of space, from emphasizing spatial totality to focusing on spatial details, and from focusing on the use of space to emphasizing the cultural characteristics of space. Research emphasizes that, within the context of digitalization, spatial design should place greater emphasis on visual aesthetics, detail processing, and cultural expression to better meet the needs and expectations of the public. This work identifies new features of the public’s perception of urban street space in the context of digitalization and accordingly provides suggestions for the future design of urban street space. The findings of this study not only enrich the foundational research content of spatial perception theory but also offer new perspectives for designers and planners in the practice of urban block renovation.

Sustainable Marketing of Agro-Tourism: The Socio-cultural Perspective

Agricultural tourism has emerged as a niche tourism phenomenon and its demand has soared across the globe. This paper focuses on the socio-cultural sustainability of agro-tourism. Key socio-cultural dimensions, social capital and authenticity, are examined based on a purposeful sample of spatially dispersed farms in the US. The study results illustrate that agrotourism holds tremendous potential to strengthen and promote local food systems by promoting traditional ways of farming, particularly objective and negotiated dimensions of authenticity, social cohesion, trust and reciprocity. Marketing strategies promoting socio-cultural consumption of agro-tourism are suggested.

SUBJECTIVE AND OBJECTIVE DIMENSIONS OF COMMUNITY RESILIENCE AT WAR TIME (BASED ON THE RESULTS OF A QUALITATIVE RESEARCH ANALYSIS)

The article is devoted to studying community resilience in the context of war in Ukraine. It analyzes both subjective and objective dimensions of resilience that affect the ability of local communities to adapt to crisis conditions. The empirical foundation of the research consists of interviews and focus groups with representatives of executive and local authorities, the public sector, business, volunteer organizations, and informal initiatives, conducted within the framework of the Polish-Ukrainian research grant «Multilevel governance of the humanitarian crisis caused by the Russian aggression on Ukraine, using examples from the Lublin Voivodeship (PL) and the Volyn Oblast (UA)» (funded by NAWA). Subjective aspects of resilience encompass public perceptions, trust among community members, and emotional resilience, which is manifested in people's willingness to support one another. Objective dimensions include infrastructural, economic, and social resources that contribute to recovery after crises. The results indicate that distinguishing between subjective and objective aspects of resilience is an effective tool for developing policies capable of supporting communities in wartime. This article aims to contribute to the understanding of the complexity of community resilience in times of crisis and social instability.

Holographic Communication: A Study of the Quality of Holographic Copies Perception

Relevance. Holography is becoming one of the most promising areas of visualization of three-dimensional objects, which justifies the emergence of a certain scientific interest in this area of research. There is a general global trend of intensifying the work of specialists on the problem of using holographic technologies in various areas of human activity. Trends in the implementation of holographic services and holographic communication today already require a revision of the principles of planning, designing and building existing communication networks, as well as approaches to the implementation of sixth-generation networks 6G, which are based on the integration of various technologies and communication networks into a single network. A separate issue is the assessment of the quality of service and the quality of perception of holographic services by both objective and subjective assessment methods. There are practically no criteria for assessing the quality of a holographic image, including scales and methods for subjective assessment of the quality of holographic services. Moreover, the properties of the holographic flow are poorly understood, and even less so its influence on communication networks and requirements for network parameters, which makes the tasks of studying traffic characteristics and assessing the quality of service of holographic services very relevant.The aim of the work is to evaluate the quality of perception of holographic conference calls using a subjective evaluation method on a model network. The work uses methods of subjective assessment of the quality of perception. The materials presented in the article reflect the results of the authors' experimental research work on studying the problem of the quality of perception of holographic copies. A description of the developed scheme of a full-scale experiment is given.Results. The data obtained as a result of the work of an expert group on assessing the quality of perception are presented. The subjective assessment of the quality of perception of a holographic image begins to deteriorate with 8 connections and becomes unsatisfactory with 12 connections, which must be taken into account when planning experimental studies of work on assessing the quality of perception. Novelty. For the first time, an assessment of the quality of perception of the provision of a holographic conferencing service was carried out using a subjective assessment method. Theoretical significance. The influence of increasing the number of holographic traffic flows on the quality of perception of the received content is analyzed. Practical significance. Expanding the possibilities for assessing the degree of user satisfaction with holographic services.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue. In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

A space-time mixed finite element method for reduced fracture flow models on nonmatching grids

This paper is concerned with the numerical solution of the flow problem in a fractured porous medium where the fracture is treated as a lower dimensional object embedded in the rock matrix. We consider a space-time mixed variational formulation of such a reduced fracture model with mixed finite element approximations in space and discontinuous Galerkin discretization in time. Different spatial and temporal grids are used in the subdomains and in the fracture to adapt to the heterogeneity of the problem. Analysis of the numerical scheme, including well-posedness of the discrete problem, stability and a priori error estimates, is presented. Using substructuring techniques, the coupled subdomain and fracture system is reduced to a space-time interface problem which is solved iteratively by GMRES. Each GMRES iteration involves solution of time-dependent problems in the subdomains using the method of lines with local spatial and temporal discretizations. The convergence of GMRES is proved by using the field-of-values analysis and the properties of the discrete space-time interface operator. Numerical experiments are carried out to illustrate the performance of the proposed iterative algorithm and the accuracy of the numerical solution.

CUAHN-VIO: Content-and-uncertainty-aware homography network for visual-inertial odometry

Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN). Content awareness measures the robustness of the network toward non-homography image content, e.g. 3-dimensional objects lying on a planar surface. Uncertainty awareness refers that the network not only predicts the homography transformation but also estimates the prediction uncertainty. The training requires no ground truth that is often difficult to obtain. The network has good generalization that enables “plug-and-play” deployment in new environments without fine-tuning. A lightweight extended Kalman filter (EKF) serves as the VIO backend and utilizes the mean prediction and variance estimation from the network for visual measurement updates. CUAHN-VIO is evaluated on a high-speed public dataset and shows rivaling accuracy to state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion blur, low network inference time (∼23 ms), and stable processing latency (∼26 ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded processor to navigate a fast autonomous MAV.

Templex-based dynamical units for a taxonomy of chaos.

Discriminating different types of chaos is still a very challenging topic, even for dissipative three-dimensional systems for which the most advanced tool is the template. Nevertheless, getting a template is, by definition, limited to three-dimensional objects based on knot theory. To deal with higher-dimensional chaos, we recently introduced the templex combining a flow-oriented BraMAH cell complex and a directed graph (a digraph). There is no dimensional limitation in the concept of templex. Here, we show that a templex can be automatically reduced into a "minimal" form to provide a comprehensive and synthetic view of the main properties of chaotic attractors. This reduction allows for the development of a taxonomy of chaos in terms of two elementary units: the oscillating unit (O-unit) and the switching unit (S-unit). We apply this approach to various well-known attractors (Rössler, Lorenz, and Burke-Shaw) as well as a non-trivial four-dimensional attractor. A case of toroidal chaos (Deng) is also treated.

Notational actants: new musical approaches through the material score

This exposition brings together a collection of images, thoughts, and descriptions of the initial stages of a doctoral research project that explores the concept of 'notational actants': materially-focused, 3-dimensional objects intended for touch-based interpretation in musical performance. The majority of the content in this exposition was created during a one-month residency at Mustarinda (Hyrynsalmi, Finland).

Boundary Value Problems in Ellipsoidal Geometry and Applications

Many applications in science, engineering, and modern technology require the solution of boundary value problems for genuine three-dimensional objects. These objects often are of or can be approximated by, an ellipsoidal shape, where the three ellipsoidal semiaxes correspond to three independent degrees of freedom. The triaxial ellipsoid represents the sphere of any anisotropic space and for this reason, it appears naturally in many scientific disciplines. Consequently, despite the complications of the ellipsoidal geometry and mainly its analysis, based on the theory of ellipsoidal harmonics, a lot of progress has been made in the solution of ellipsoidal boundary value problems, due to its general applicability. In this mini-review, we aim to present to the scientific community the main achievements towards the investigation of three such physical problems of medical, engineering and technological significance, those comprising intense research in (a) electroencephalography (EEG) and magnetoencephalography (MEG), (b) creeping hydrodynamics (Stokes flow) and (c) identification of metallic impenetrable bodies, either embedded within the Earth’s conductive subsurface or located into a lossless air environment. In this context, special expertise and particular skills are needed in solving open boundary value problems that incorporate the ellipsoidal geometry and the related harmonic analysis, revealing the fact that there still exists the necessity of involving with these issues.