Object Points Research Articles

A novel metric for 6D pose estimation

Abstract Current state-of-the-art evaluation methods for 6D pose estimation have several significant drawbacks. Existing error metrics can produce near-zero errors for poor pose estimations and are heavily dependent on the object point cloud used, resulting in vastly different outcomes for different objects. Furthermore, false detections are not considered at all. Evaluating pose estimators is crucial as it directly impacts the reliability and effectiveness of applications in robotics, augmented reality, and object manipulation tasks. Accurate evaluation ensures that pose estimators can be trusted to perform well in real-world scenarios, leading to better system performance and user satisfaction. In this paper, we conduct experiments to provide insights into how these metrics behave under isolated errors. We also introduce a novel evaluation approach with a metric independent of point clouds, making it applicable to a broader range of use cases than current metrics.

Advancements in Autonomous Driving Systems: Enhancing Environmental Perception with Lidar Integration and Innovative Algorithms

This paper discusses the intricacies of autonomous driving systems, focusing on the four core modules: perception, sensing, decision-making, and control. The sensing module integrates various sensors like LiDAR, cameras, millimeter-wave radars, IMU, and GPS to gather essential data. The paper emphasizes the role of LiDAR in 3D object detection and point cloud segmentation, which are crucial for environmental perception. It also addresses the challenge of fusing data from multiple sensors into a unified coordinate system for effective decision-making. This research presents several significant advances in environmental perception. First there is a single-stage 3D object detection algorithm using sparse convolutional neural networks for improved speed and accuracy. Moreover there is a two-stage 3D object detection method focuses on detecting difficult targets through target center prediction. This method employs a novel neural network architecture, AOAP-SCNN, for feature extraction. Furthermore there is a point cloud instance segmentation algorithm (a method that separates and identifies individual objects in 3D space) utilizes a front view approach and a DLA network with Tiny-PointNet for feature extraction. Finally there is also an automatic calibration algorithm for LiDAR and camera systems based on segmentation, designed for real-time operation on embedded platforms, which corrects the extrinsic matrix and improves the self-calibration system's practical value. Building upon these technical innovations, the paper provides a comprehensive analysis of intelligent vehicle technology including the history of development, the importance of environmental perception, and the application and limitations of LiDAR in intelligent vehicles.

The system of filling the information model of construction structural elements

The article considers the need for information filling of the digital model of the building object. The effectiveness of laser scanning for the real display of the state of building structures and their adequacy to the design data was noted. The effectiveness of laser scanning for the formation of a cloud of spatial points of objects for the further formation of a three-dimensional digital model was noted. Examples of laser scanning of building objects and subsequent reproduction using software of volumetric digital elements from the obtained point cloud are given. It is proposed to consider the filling system of the information model on the example of the developed technology of installation of the structural coating of light metal structures. On the basis of the developed installation technological equipment, control indicators, features of the installation technology, structural features of the building object are determined, which allows entering such data for consideration in the monitoring system of the installation process. All this together makes it possible to develop a generalized database of both the object as a whole and its components, which will take into account the compliance of the manufactured structure with the design data, the preservation of its properties before the installation process, during installation during operation, strengthening, and disposal.

Optimized Frontier-Based Path Planning Using the TAD Algorithm for Efficient Autonomous Exploration

A novel path-planning method utilizing the trapezoid, adjacent, and distance, (TAD) characteristics of frontiers is presented in this work. The method uses the mobile robot’s sensor range to detect frontiers throughout each exploration cycle, modifying them at regular intervals to produce their parameters. This well-thought-out approach makes it possible to choose objective points carefully, guaranteeing seamless navigation. The effectiveness and applicability of the suggested approach with respect to exploration time and distance are demonstrated by empirical validation. Results from experiments show notable gains over earlier algorithms: time consumption decreases by 10% to 89% and overall path distance for full investigation decreases by 12% to 74%. These remarkable results demonstrate the efficacy of the suggested approach and represent a paradigm change in improving mobile robot exploration in uncharted territory. This research introduces a refined algorithm and paves the way for greater efficiency in autonomous robotic exploration. This study opens the door for more effective autonomous robotic exploration by introducing an improved algorithm.

Point Cloud Wall Projection for Realistic Road Data Augmentation.

Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task. Although there are a few state-of-the-art techniques to generate points from synthetic objects using LiDAR point clouds, limitations such as the need for intense compute power still persist in most cases. This paper suggests a new framework to address these limitations in the existing literature. The proposed framework contains three major modules, namely position determination, object generation, and synthetic annotation. The proposed framework uses a spherical point-tracing method that augments 3D LiDAR distant objects using point cloud object projection with point-wall generation. Also, the pose determination module facilitates scenarios such as platooning carried out by the synthetic object points. Furthermore, the proposed framework improves the ability to describe distant points from synthetic object points using multiple LiDAR systems. The performance of the proposed framework is evaluated on various 3D detection models such as PointPillars, PV-RCNN, and Voxel R-CNN for the KITTI dataset. The results indicate an increase in mAP (mean average precision) by 1.97%, 1.3%, and 0.46% from the original dataset values of 82.23%, 86.72%, and 87.05%, respectively.

ELEMENTS OF SUCCESS IN SOLIDARY ECONOMY VENTURES

The gap in this research is related to the identification of the successful elements that govern the actions and activities of solidary enterprises, in parallel with what has already been proposed in the literature, using selective garbage collection cooperatives and associations as a basis. Thus, the question of this research questioned whether cooperatives and associations of selective garbage collection present elements of success due to the integral observation of the basic principles proposed for projects with solidary characteristics, already proposed in the literature. The objective points to the investigation of the guiding principles of solidary enterprises and their comparison with the perceived success elements. Therefore, this research is characterized as being of an applied, exploratory, and descriptive nature, with a qualitative approach, whose field of study involved fifteen associations and cooperatives located in the fifteen municipalities of Paraná bathed by the reservoir of the Itaipu Hydroelectric Plant, in the western region of the state. Fifteen interviews were carried out, and from the content analysis, with the help of the Iramuteq software, it was possible to diagnose the regional reality and prove that these projects are not supported by the basic principles proposed by the literature on solidarity projects, but their true elements of success point to formalization, public policies, interpersonal relationships, production, leadership, popular support, and financial security. This research showed that the theoretical principles proposed for the effective characterization of solidary economy enterprises are, at least in this region and in this context, being replaced and considerably rooted by new elements of success.

Husserl: Psychological Epoché and Pure Psychology

Abstract The experience error is the fallacy of attributing to our experience of a thing what we know to be true of that thing from an objective point of view. This paper argues that the “method of purification” advocated by Edmund Husserl for psychology is nothing other than avoidance of the experience error. The purity of psychology is not philosophical (transcendental) purity. Psychology remains in the natural attitude; it is pure if it is true to the subjective, psychical, genuinely lived content of experience and thus is purely descriptive. But that is easier said than done. Due to the dominance of the modern scientific outlook, the experience error is so insidious that, as the paper argues in a final section, Husserl himself was not immune to it.

The Relevance of the Passing of Time in Criminal Law, with Special Reference to Due Process

The requirement for the conclusion of criminal proceedings within a reasonable time appears as a component of the right to a fair trial in international human rights documents as well as among the national fundamental rights. The passage of time and the prolongation of criminal proceedings are considered by courts as mitigating factors during sentencing. However, taking these factors into account as mitigating circumstances is not unproblematic, as there is no objective point in time after which one can definitively state that the criminal authorities violated the requirement for adjudication within a reasonable time. In my study, I am investigating the criteria by which the European Court of Human Rights (ECHR) examines compliance with the requirement of reasonable time and how the prolongation of proceedings is treated as a mitigating factor in Hungarian judicial practice.

DPFANet: Deep Point Feature Aggregation Network for Classification of Irregular Objects in LIDAR Point Clouds

Point cloud data acquired by scanning with Light Detection and Ranging (LiDAR) devices typically contain irregular objects, such as trees, which lead to low classification accuracy in existing point cloud classification methods. Consequently, this paper proposes a deep point feature aggregation network (DPFANet) that integrates adaptive graph convolution and space-filling curve sampling modules to effectively address the feature extraction problem for irregular object point clouds. To refine the feature representation, we utilize the affinity matrix to quantify inter-channel relationships and adjust the input feature matrix accordingly, thereby improving the classification accuracy of the object point cloud. To validate the effectiveness of the proposed approach, a TreeNet dataset was created, comprising four categories of tree point clouds derived from publicly available UAV point cloud data. The experimental findings illustrate that the model attains a mean accuracy of 91.4% on the ModelNet40 dataset, comparable to prevailing state-of-the-art techniques. When applied to the more challenging TreeNet dataset, the model achieves a mean accuracy of 88.0%, surpassing existing state-of-the-art methods in all classification metrics. These results underscore the high potential of the model for point cloud classification of irregular objects.

Artificial Intelligence applications for Information management in sustainable Supply Chain Management: A Systematic Review and future research agenda

In a Sustainable Supply Chain (SSC) context, information management offers a unique perspective on the digital economy and information management. Artificial intelligence (AI) is developing into a more robust digital field to facilitate quick information access and intelligent decisions in expanding commercial contexts. These days, Supply Chains (SC) would crumble without robust information systems. Applying AI and information management is crucial in determining the direction of sustainable supply chain management (SSCM). A systematic literature review (SLR) of the use of AI in SSCM is conducted in this research. The authors can identify crucial factors of the present literature using bibliometric and network analysis. AI is essential to the SSC to address sustainability challenges and manage the large volumes of data produced by numerous industrial processes. In the corpus of research that is already accessible, there is currently no comprehensive and bibliometric analysis of the potential for AI techniques for information management in SSC. Scientific publications were analysed from an objective point of view. Based on our results, we have drafted a proposal for an AI supply chain framework. Researchers, policymakers, and SCM practitioners may all benefit from the approach. This study is the first to analyse AI applications for information management in SSCM. In consideration of this, organizations are now exploring AI capabilities to improve operational efficiency and innovate their processes. This will assist industry people in understanding how AI methods support SC processes in their optimization to attain sustainability in SC practices.

Comprehensive Evaluation Research on the Sustainable Development Level of Rural Energy Based on CRITIC-MABAC in Henan Province

Energy shortages are a key issue at present, and climate problems caused by the extraction and use of traditional fossil energy sources are also creating new challenges for rural development, with sustainable rural energy development being a priority at present. This paper takes the sustainable development of rural energy as the research object, and evaluates the development of rural energy in each prefecture-level city in Henan Province. On the one hand, rural development is viewed from the perspective of energy, on the other hand, the evaluation results provide a reference for each prefecture-level city, and most of the energy data can be obtained directly and with a high degree of accuracy, based on the CRITIC – MABAC methodology to determine the weights from an objective point of view and to rank the evaluation results, which is capable of obtaining the most accurate evaluation results. Finally, we used energy-related data from Henan Province in 2022 to conduct empirical analyses, and verified that Shangqiu City has a high level of sustainable rural energy development, which verifies the feasibility of the methodology proposed in this paper, and gives relevant recommendations.

PPDM++: Parallel Point Detection and Matching for Fast and Accurate HOI Detection.

Human-Object Interaction (HOI) detection aims to understand human activities by detecting interaction triplets. Previous HOI detection methods adopt a two-stage instance-driven paradigm. Unfortunately, many non-interactive human-object pairs generated by the first stage are the main obstacle impeding HOI detectors from high efficiency and promising performance. To remedy this, we propose a novel top-down interaction-driven paradigm, detecting interactions first and bridging interactive human-object pairs through interactions. We formulate HOI as a point triplet human point, interaction point, object point and design a Parallel Point Detection and Matching (PPDM) framework. We further take advantage of two-stage methods and propose a novel framework, PPDM++, that detects the interactive human-object pairs by PPDM, then extracts region features for each pair to predict actions. The core of PPDM/PPDM++ is to convert the instance-driven bottom-up paradigm to an interaction-driven top-down paradigm, thus avoiding additional computation costs from traversing a tremendous number of non-interactive pairs. Benefiting from the advanced paradigm, PPDM/PPDM++ has achieved significant performance gains with high efficiency. PPDM-DLA-34 has achieved 19.94 mAP with 42 FPS as the first real-time HOI detector, and PPDM++-SwinB achieves 30.1 mAP with 17 FPS on HICO-DET dataset. We also built an application-oriented database named HOI-A, a supplement to the existing datasets.

TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment.

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this article, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios.

Evaluation the Role of Serum Epidermal Growth Factor Like Domain 7 as a New Non-Invasive Diagnostic Marker for Hepatocellular Carcinoma in Egyptian Cirrhotic Patients

Abstract Background Hepatocellular carcinoma (HCC) is a significant global health concern, ranking as the sixth most common cancer worldwide. In Egypt, it is the fourth most prevalent malignancy. The rising incidence of HCC has been attributed to advancements in screening programs, improved diagnostic tools, increased survival rates in cirrhotic patients, and a higher prevalence of hepatitis C virus (HCV) infections. Objective Point this study is to assess job serum epidermal development factor like space 7 as a demonstrative marker in HCC Egyptian cirrhotic patients. Methods A total of 90 subjects were enrolled in this study, categorized into two groups: Group A (HCC), comprising 45 patients with Hepatocellular carcinoma, and Group B (Cirrhotic), comprising 45 patients with liver cirrhosis without HCC. The study subjects underwent history taking, physical examination, routine laboratory tests, measurement of AFP and EGFL7 levels, and radiological evaluations. Results EGFL7, a molecule with signaling and EGF-like domains, was significantly upregulated in HCC tissues compared to cirrhotic tissues. High EGFL7 expression was associated with adverse clinicopathological features and poor prognosis in HCC patients. The study demonstrated that EGFL7 exhibited higher diagnostic performance in distinguishing between cirrhosis and HCC compared to AFP. Moreover, EGFL7 levels were positively correlated with the Child-Pugh score and the size of hepatic lesions in the HCC group. Conclusion This research highlights that serum EGFL7 is substantially elevated in HCC patients, particularly those with co-existing conditions such as diabetes, hypertension, or viral markers. EGFL7 emerges as a more sensitive and accurate marker than AFP for early HCC detection. Incorporating EGFL7 into diagnostic algorithms could enhance the effectiveness of surveillance programs for early HCC detection, ultimately leading to improved clinical outcomes and patient management.

Multi-line laser scanning reconstruction with binocularly speckle matching and trained deep neural networks

A multi-line laser scanning system for 3D topography measurement is proposed. This method not only has the advantages of high precision of laser scanning technology, but also has high reconstruction efficiency. In this paper, speckle reconstruction technique, multi-line laser technique and Biocular reconstruction technique are used to construct a 3D reconstruction system, and test equipment is built, and the problems existing in the system establishment process are actually studied. In order to solve the problem of mismatching in binocular multi-line laser matching, a method to sort out the correspondence of multiple laser lines in binocular images based on speckle matching results is proposed. In order to optimize the multi-line laser matching effect, a speckle matching network based on deep learning is proposed, which integrates the grayscale images of the left and right cameras as supplementary information, and takes the speckle image and grayscale image as the input of the network model to obtain more accurate and edge-complete matching results. Finally, the matching results of the multi-line laser and the camera calibration parameters were used to reconstruct the object point cloud. Experimental results show that the proposed speckle matching method can make binocular multiline laser point cloud reconstruction more robust and stable than the traditional method, and through the accuracy analysis of the system, it is proved that the average measurement accuracy of the proposed method can reach 0.05 mm.

Towards automatic urban tree inventory: Enhancing tree instance segmentation via moving object removal and a chord length-based DBH estimation approach

To enhance urban forestry efficacy in Hong Kong, implementing a paradigm shift towards an automated urban tree inventory that utilizes advanced sensing technologies and artificial intelligence is essential for streamlined data collection and analysis. This study advances this objective by creating a comprehensive framework for estimating diameter at breast height (DBH) and extracting tree images. This framework encompasses five key stages: (1) data acquisition utilizing StructXray, a mobile mapping system equipped with a 360° camera and a multi-beam flash LiDAR sensor; (2) vegetation point clouds extraction using deep learning techniques; (3) individual tree segmentation through machine learning algorithms; (4) DBH estimation; and (5) tree image extraction. Six datasets were collected, yielding tree detection precision, recall and F1 score of 0.88, 0.95 and 0.91 respectively. The presence of moving objects within the 3D point cloud map, exhibiting diverse geometric structures, hinders precise vegetation point cloud segmentation by the pointwise neural network. To tackle this challenge, SalsaNext was employed to rectify the predictions of a pointwise neural network, specifically RandLA-Net in this study, eliminating 91 % of misclassified moving object point clouds and completely removing them from 47 % of affected individual tree point clouds. Additionally, a chord length-based method was proposed to enhance DBH estimation accuracy by dividing the point cloud slice into sectors and summing the chord lengths to estimate the tree trunk perimeter. Compared to the ellipse least squares fitting method, this approach reduced the root-mean-square error of the estimated DBH by 1.31 cm.

Speckle noise suppression method in holographic display using optimized pixel processing

In this paper, we propose a method to suppress the speckle noise in computer-generated holography based on optimized pixel processing. The recorded object is divided into two object point groups by the spatial separation of pixels. The complex amplitude of the light field for each object point group is recorded as a sub-computer-generated hologram (sub-CGH). The odd and even rows of the sub-CGH are scanned from opposite directions. The magnitude of each visited pixel is set to 1, while preserving the exact phase value. The resulting error is diffused to the neighboring pixels that have not been visited before. Thus, the complex amplitude sub-CGHs are converted into the phase-only sub-CGHs. In the reconstruction process, the final sub-CGHs are loaded on the spatial light modulator sequentially. Experimental results show that our proposed method outperforms the traditional pixel processing and error diffusion methods in terms of the image quality and light efficiency.

A Small-Object-Detection Algorithm Based on LiDAR Point-Cloud Clustering for Autonomous Vehicles.

3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. Therefore, a small-object-detection algorithm based on clustering is proposed. Firstly, a new segmented ground-point clouds segmentation algorithm is proposed, which filters out the object point clouds according to the heuristic rules and realizes the ground segmentation by multi-region plane-fitting. Then, the small-object point cloud is clustered using an improved DBSCAN clustering algorithm. The K-means++ algorithm for pre-clustering is used, the neighborhood radius is adaptively adjusted according to the distance, and the core point search method of the original algorithm is improved. Finally, the detection of small objects is completed using the directional wraparound box model. After extensive experiments, it was shown that the precision and recall of our proposed ground-segmentation algorithm reached 91.86% and 92.70%, respectively, and the improved DBSCAN clustering algorithm improved the recall of pedestrians and cyclists by 15.89% and 9.50%, respectively. In addition, visualization experiments confirmed that our proposed small-object-detection algorithm based on the point-cloud clustering method can realize the accurate detection of small objects.

Boundary points guided 3D object detection for point clouds

3D object detection in LiDAR point clouds is crucial for computer vision tasks such as autonomous driving. The two-stage approach with point cloud completion achieves remarkable performance by generating semantic surface points for foreground objects or learning bird’s eye view shape heat map labels. However, these methods require additional completion datasets, leading to substantial computation and memory demands. In this context, we propose a Boundary Points Guided 3D (BPG3D) object detection method that complements point cloud boundary information without the need for additional data. Specifically, we generate Region of Interest (RoI) boundary points to aggregate the neighbor voxel information at the RoI boundary during the refinement stage to complement the missing boundary information. Meanwhile, we design a Dual Feature Selection (DFS) module to adaptively fuse RoI grid point features and RoI boundary point features for bounding box refinement with negligible computational cost. Additionally, inspired by tensor decomposition theory, we use low-rank tensors to reconstruct high-rank tensors in the point cloud feature encoder to enhance contextual semantic information. The proposed method achieves 65.81% mAP on KITTI Test Set, obtaining a good trade-off between accuracy and efficiency.

MXene and Polymer Collision: Sparking the Future of High‐Performance Multifunctional Coatings

AbstractThe development of nanomaterials is crucial to upgrading of modern industry. MXene nanosheets have attracted significant attention due to their superb resistance to permeation, diverse surface chemical properties, impressive mechanical properties, and metal‐like electrical and thermal conductivity, etc., providing unique advantages in various technical fields. When MXene nanosheets are combined with polymers to form functional coatings, their applications span multiple fields, including anticorrosion, wear resistance, flame‐retardancy, electromagnetic interference (EMI) shielding, and de‐icing. This review, in conjunction with MXene's barrier property, lubricity, thermal stability, conductivity, and photothermal conversion property, discusses in detail the various applications of MXene‐based polymer coatings. Additionally, it examines the dispersion and interaction of MXene within polymer coatings and the role of functionalized MXene in polymers. Finally, based on the current research status and emerging needs, the development challenges and future research opportunities of MXene‐based polymer coatings in a targeted manner are discussed. This review aims to summarize the existing research results and put forward an objective and fair point of view, providing a constructive reference for upgrading modern nanofunctional polymer coatings.