Three-dimensional Objects Research Articles

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.

A Brief Discussion on the Problems and Reform Suggestions of Physics Experiment Teaching in Colleges and Universities

With the cooperation of physics experiment teaching and theoretical teaching, colleges and universities put forward three dimensions of experiment teaching objectives, that is, optimize the experiment content and experiment equipment, attach importance to the quality control of the whole process of experiment teaching and establish a diversified experiment teaching evaluation model, and determine the teaching objectives of physics experiment courses. Under the trend of education reform, the physics experiment teaching in colleges and universities is constantly innovating, developing comprehensive experiments and designed experiments, improving experimental equipment, and optimizing the test work of experimental courses. This paper discusses the work of physics experiment teaching in colleges and universities, analyzes the focus of the reform of physics experiment teaching, and probes into the specific strategies of the reform, hoping to provide strong support for promoting the reform of physics experiment teaching in colleges and universities.

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 (AI) 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.

Engaging with discursive complexities in mental health accessibility: Implications for acquired brain injury.

The psychosocial needs of people with acquired brain injury (ABI) have been neglected based on ableist assumptions of incapability to participate in mental health treatment. Although people without disabilities benefit from evidence-based mental health supports, these treatments remain inaccessible for those with disabilities after ABI. Discursive simplifications used in dominant conceptualisations of health and disability may maintain this inaccessibility. This paper examines the role of discursive constraints in concealing the complexities of ABI recovery, undermining the gradients of mental health exclusion among different ABI subpopulations, and muddying possibilities for enhancing mental health accessibility. An alternate discourse that challenges disabling societies in service of centring the whole person is proposed. Discursive opportunities are thus created by conceptualising the objective and subjective dimensions of disability as intermeshed, providing both the motivation to incentivise mental health inclusion, as well as a method to achieve it. By recognising the unavoidable impact of bodily impairments on social participation, participatory ideals can be actualised by accommodating ABI-related disabilities in mental health treatments. The possibilities for transformative research and practice are illuminated through examples of mental health treatments that have been preliminarily adapted using accommodations, and a research agenda for realising these possibilities is proposed.

Ghost Removal from Forward-Scan Sonar Views near the Sea Surface for Image Enhancement and 3-D Object Modeling

Underwater sonar is the primary remote sensing and imaging modality within turbid environments with poor visibility. The two-dimensional (2-D) images of a target near the air–sea interface (or resting on a hard seabed), acquired by forward-scan sonar (FSS), are generally corrupted by the ghost and sometimes mirror components, formed by the multipath propagation of transmitted acoustic beams. In the processing of the 2-D FSS views to generate an accurate three-dimensional (3-D) object model, the corrupted regions have to be discarded. The sonar tilt angle and distance from the sea surface are two important parameters for the accurate localization of the ghost and mirror components. We propose a unified optimization technique for improving both the measurements of these two parameters from inexpensive sensors and the accuracy of a 3-D object model using 2-D FSS images at known poses. The solution is obtained by the recursive updating of sonar parameters and 3-D object model. Utilizing the 3-D object model, we can enhance the original images and generate synthetic views for arbitrary sonar poses. We demonstrate the performance of our method in experiments with the synthetic and real images of three targets: two dominantly convex coral rocks and a highly concave toy wood table.

EFMF-pillars: 3D object detection based on enhanced features and multi-scale fusion

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPDarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm’s average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable.

A Fast Grasp Planning Algorithm for Humanoid Robot Hands.

Grasp planning is crucial for robots to perform precision grasping tasks, where determining the grasp points significantly impacts the performance of the robotic hand. Currently, the majority of grasp planning methods based on analytic approaches solve the problem by transforming it into a nonlinear constrained planning problem. This method often requires performing convex hull computations, which tend to have high computational complexity. This paper proposes a new algorithm for calculating multi-finger force-closure grasps of three-dimensional objects based on humanoid multi-fingered hands. Firstly, sufficient conditions for the multi-finger force-closure grasps of three-dimensional objects are derived from a point contact model with friction. These three-dimensional force-closure conditions are then transformed into two-dimensional plane conditions, leading to a simple algorithm for multi-finger force-closure determination. This method is purely based on geometric analysis, resulting in low computational demands and enabling the rapid assessment of force-closure grasps, which are beneficial for real-time applications. Finally, the algorithm is validated through two case studies, demonstrating its feasibility and effectiveness.

Quantifying multifractal anisotropy in two dimensional objects.

An efficient method of exploring the effects of anisotropy in the fractal properties of 2D surfaces and images is proposed. It can be viewed as a direction-sensitive generalization of the multifractal detrended fluctuation analysis into 2D. It is tested on synthetic structures to ensure its effectiveness, with results indicating consistency. The interdisciplinary potential of this method in describing real surfaces and images is demonstrated, revealing previously unknown directional multifractality in data sets from the Martian surface and the Crab Nebula. The multifractal characteristics of Jackson Pollock's paintings are also analyzed. The results point to their evolution over the time of creation of these works.

Build orientation optimization considering thermal distortion in additive manufacturing

Additive manufacturing (AM) technology enables the fabrication of three-dimensional objects with complex shapes and has been extensively applied in various industries. AM is a layer-wise fabrication process where a variety of factors affect manufacturing performance and product quality. One of the most important factor is the thermal distortion, which is caused by the high temperature gradients in the fabrication process. The thermal distortion is also influenced by the support structure of the printing object, and this distortion varies depending on the chosen build orientation. Additionally, for the overhang regions, extra supports are required for printing and will be removed in the post-processing. For the 3D printing process, the thermal distortion and support requirements are interconnected and linked to build orientation. To investigate suitable build orientation, the thermal distortion and support are quantified, and a multi-objective build orientation optimization method is proposed to obtain representative orientations. Based on the proposed method, 9 typical 3D shapes are evaluated. In addition, the single-objective build orientation optimization problem is studied and compared, and the influence of slicing layers per stage on the simulation accuracy and efficiency is discussed. The effectiveness and applicability of the method are verified, and representative directions can be obtained for different fabrication purposes.

The Experience of Housing Discrimination and Housing Deprivation Across Social Groups in Ireland

AbstractThis study focuses on housing inequalities across diverse social groups with respect to two housing indicators: subjective housing discrimination; and objective housing deprivation. By scrutinizing the correspondence in inequalities between the two indicators, we enhance the measurement of housing inequality and at the same time we shed light on the relationship between subjective and objective dimensions of housing— a relationship that has received little empirical examination so far. Existing research suggests that housing discrimination can contribute to housing deprivation through two channels: inadequate information and limited economic resources. Our focus is the post-2008 housing market crash era in Ireland. We use two separate large-scale individual-level surveys, the QNHS (2004, 2010, 2014) for analysing housing discrimination and SILC (2014, 2015) for analysing housing deprivation. Our findings reveal a robust convergence between perceptions of housing discrimination and objective housing deprivation across various social groups. Traditionally vulnerable groups—such as young individuals, those with disabilities, non-natives (excluding EU citizenship holders), single parents, and individuals without children—experience both discrimination and deprivation. Notably, certain groups exhibit persistent disparities in discrimination and disadvantage, even after considering factors like human capital, regional and tenure differences.

Recent onset mental illness severity: pilot study on the role of cognition, sensory modulation, and daily life participation.

Early detection of individuals at risk for onset of severe illness is crucial for prevention and early intervention, aiming to mitigate the long-term impact on both the individual and the community. While well-established models exist for predicting the onset and prolonged severity of illness, there is a gap in understanding illness-onset severity. This pilot study aimed to investigate premorbid objective and subjective dimensions of participation in daily life occupations, as well as sensory and cognitive functions as potential markers of the recent-onset mental illness severity. A total of 50 participants (men: N=26, 52%; women: N=24, 48%), aged 18-40 (M=26.2, SD=5.8) with recent-onset mental illness completed standard, well-established assessments of illness severity, cognitive biases and failures, neurocognitive status, participation in daily life, and sensory responsiveness thorough cross-sectional design. The differences between the groups of the illness severity were explored with descriptive statistics, followed by a Kruskal-Wallis test. Discriminant analysis was used suggesting a multi-varied model for the separation between the groups of illness severity. Three groups of illness severity exhibited differences in premorbid cognitive functions (F(2)=5.8, p<.01) and participation diversity (F(2)=3.8, p<.05). Combining these two indices explained 92% of the variance between the groups (Wilks' Λ = .68, χ2(4) = 17.7, p=.001), accurately classifying mild to marked illness severity (62.5-88.5%). The study contributes to revealing factors involved in the formation of more severe mental illness and suggesting possible avenues for early intervention and prevention. Cognitive biases and sensory modulation dysfunction may contribute to the illness formation. Still, the most effective markers of more severe mental illness onset are functional cognition and limited participation diversity. Since addressing these markers is a unique specialization within occupational therapy, the findings highlight the potential contribution theprofession can make to the early identification of the most vulnerable populations.

The 3D tree dataset: an artistic experiment using a voxel-based GAN

AbstractIn visual culture, Generative Adversarial Networks (GANs) have been used to generate two-dimensional images, video, and three-dimensional forms. Whilst there are a relatively large number of conditional datasets of two-dimensional images, there are fewer datasets of three-dimensional objects available for training, evaluation, and practical use. In this paper, we introduce a synthetic dataset of 3D trees that provide novel geometric challenges for machine learning and describe our use of this dataset in training and qualitative evaluation via a public exhibition and survey. The 3D Tree Dataset was made using random variations of 76 bespoke tree templates based on art historical references to favor interesting, beautiful, and varied trunk and branch shapes. In generating and experimenting with this dataset, we wanted to know how a geometrically complex series of organic forms would challenge voxel-GANs compared to existing datasets comprising industrial objects such as chairs and cars. As an interdisciplinary project between visual arts and computer science, we used 3D printing and animation to visualize and communicate our outputs to non-specialist audiences. In this paper, we describe and share the 3D Tree Dataset, outline the process of generating the dataset, describe the GAN architecture and application used to test the dataset and discuss the results relative to our artistic and cultural goals.

Bidirectional evaluation and differentiation analysis of marine economic resilience in China

With the continuous improvement of the marine economy, the role of marine economic resilience is becoming ever more prominent. This study evaluates China’s marine economic resilience based on a bidirectional evaluation index system, and analyzes spatiotemporal characteristics and regional differences. The results show that: (1) marine economic resilience is increasing in the system object dimension and the resilience attribute dimension in China. Strengthening the protection of marine resources and the environment is important for improving marine economic resilience; (2) the development of marine economic resilience among 11 coastal provinces is unbalanced, forming a three-polar pattern with Shandong as the core in the north, Shanghai as the core in the east, and Guangdong as the core in the south; (3) the difference between the northern marine economic circle and the southern marine economic circle is the main reason for the overall difference.

From rationality to identity: the impact of using community health services for the aged on the well-being of older adults in China.

To better understand the outcome benefits of community health services for the aged (CHSA) and provide bottom-up identity for development, this study examined the impact of using CHSA on well-being of older adults from both objective and subjective dimensions. Data from 1,411 people aged 60+ in Shaanxi, China was used in this study. The OLS estimate was used to analyze the impact of using CHSA on well-being of older adults. Then, the instrumental variable estimate was further hired to examine associations among variables to address the potential endogeneity concerns. The heterogeneity among disabled and non-disabled group was also estimated. For objective well-being, the mental health (β = 0.44) and financial well-being (β = 0.59) of older people using CHSA were significantly higher than those not using any service. The impact on objective well-being were more significant in non-disabled group. For subjective well-being, life meaning (β = 0.54) and security (β = 0.72) were positively associated with CHSA utilization, while independence index (β = -0.33) was negatively related with CHSA utilization. The subjective well-being of disabled group using services increased more than non-disabled group. Findings underscore the positive, albeit group-selective, role of CHSA in improving well-being of older people in China and imply the necessity of high-quality development of CHSA and the targeted differentiation strategy.

Ultrasonic Levitation as a Handling Tool for In-Space Manufacturing Processes

Abstract 3D printing is one of the key technologies in space exploration. The disparity in gravitational forces between Earth and space presents both challenges and opportunities with regard to material handling. This article examines the potential of employing ultrasonic levitation as a handling tool for substrate-free additive manufacturing processes in microgravity environments. Through preliminary experiments, we demonstrate the feasibility of manipulating polymer powders using acoustic fields while concurrently melting the levitated material. Subsequent experiments conducted in our drop tower facility confirm our ability to manipulate particles with acoustic traps under microgravity conditions. Building upon these findings, we outline plans to further advance our research using an expanded acoustic levitation system capable of three-dimensional object manipulation. Our objectives include moving and orienting large components beyond the wavelength limit in microgravity, manipulating granular raw material while melting it in proximity to the print part, and achieving a semi-continuous fusion of print material with the print part. Therefore, we present an intelligent control strategy based on the results of a digital twin simulation. Furthermore, we utilize a stereo camera combined with computer vision as feedback for the control system to ensure precise handling of the manipulated objects and particles. This study represents a significant advance toward the realization of efficient substrate-free additive manufacturing processes in microgravity environments, with potential applications for in-space manufacturing. Ultimately, this could result in long-term space missions becoming less reliant on supply deliveries, thus reducing cost and additionally enabling faster response to unforeseen issues.

Rapid hologram generation through backward ray tracing and adaptive-resolution wavefront recording plane

Abstract An advanced method for rapidly computing holograms of large three-dimensional (3D) objects combines backward ray tracing with adaptive resolution wavefront recording plane (WRP) and adaptive angular spectrum propagation. In the initial phase, a WRP with adjustable resolution and sampling interval based on the object’s size is defined to capture detailed information from large 3D objects. The second phase employs an adaptive angular spectrum method (ASM) to efficiently compute the propagation from the large-sized WRP to the small-sized computer-generated hologram (CGH). The computation process is accelerated using CUDA and OptiX. Optical experiments confirm that the algorithm can generate high-quality holograms with shadow and occlusion effects at a resolution of 1024 × 1024 in 29 ms.

3D Object Detection via Residual SqueezeDet

Three-dimensional object detection is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. This paper introduces Residual SqueezeDet, a novel network architecture that enhances the performance of 3D object detection on the KITTI dataset. Building upon the efficient SqueezeDet framework, we propose the Residual Fire module, which incorporates skip connections inspired by ResNet architectures into the original Fire module. This innovation improves gradient flow, enhances feature propagation, and allows for more effective training of deeper networks. Our method leverages point cloud and image-based features, employing a Residual SqueezeDet to effectively capture local and global context. Extensive experiments on the KITTI dataset demonstrate that Residual SqueezeDet significantly outperforms the original SqueezeDet, with particularly notable improvements in challenging scenarios. The proposed model maintains computational efficiency while achieving state-of-the-art performance, making it well-suited for real-time applications in autonomous driving. Our work contributes to the field by providing a more accurate and robust solution for 3D object detection, paving the way for improved perception systems in dynamic environments.

Job quality and institutional investors: Evidence in 17 OECD countries, 1993-2017

This article investigates the relationship between the share of assets held by different institutional investors as a proportion of GDP and a synthetic index of job quality in 17 OECD countries from 1993 to 2017. Our first contribution is to provide a new, multidimensional composite indicator of job quality based only on objective dimensions. According to this measure, a continuous decline in job quality is observed in many OECD countries. Second, the emergence of institutional investors as central financial actors since the 1980s has significantly affected labour relations. In this regard, we argue that the increasing influence of institutional investors through their effects on wages and jobs is associated with a lower level of job quality. Using fixed-effects OLS and IV regressions, we find little support that the share of asset holdings by institutional investors is correlated with a lower level of job quality, mainly due to the small magnitude of the coefficient estimates. Finally, we find that the job quality-reducing effect of the share of assets held by institutional investors is more pronounced in countries that have experienced a decline in union bargaining power, again with a small magnitude of our different estimates.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Three-dimensional object reconstruction based on spin–orbit interaction of light

Three-dimensional (3D) reconstruction has received extensive attention in recent years and is of great significance to various application fields. We propose a simple method of 3D object reconstruction based on spin–orbit interactions occurring from light reflection at the air–glass interface. By rotating the object, edge images of the object in various orientations are obtained; we overlap these edge images to ultimately reconstruct the 3D object. This 3D image detection based on spin–orbit interaction of light provides a low energy consumption, low cost, and more algorithmic method compared to traditional 3D image processing. This can be used in fields such as medicine, autonomous vehicles, planetary observation, and other areas.