Shape Classification Research Articles

Simple Models of Directly Measured Energy Landscapes for Different Shaped Particles in Nonuniform AC Electric Fields.

We report direct measurements of potential energy landscapes for different shaped colloidal particles interacting with nonuniform AC electric fields. Epoxy particle shapes investigated include disks, ellipses, squares, rectangles, and rhombuses, which are all part of the superelliptical prism shape class and are chosen to systematically vary particle anisotropy and corner features. The measurement configuration consists of noninteracting single particles sedimented onto microscope slides within electric fields between parallel coplanar electrodes. Thermally sampled positions and orientations of single particles in nonuniform fields are tracked in an optical microscope, and measured potential energy landscapes are obtained via Boltzmann inversions. We develop a new analytically simple model that captures all measured energy landscapes for superelliptical prism shaped colloidal particles with electrostatic double layers. The model recovers known validated potentials for spherical and ellipsoidal particles, and therefore captures energy landscapes for a variety of different colloidal particle sizes, shapes, and materials reported in prior studies.

Numerical investigation on effect of non-parallelism on natural transition in boundary layers around underwater axisymmetric bodies

For both fluid mechanics and practical engineering problems, it is important to study the effect of non-parallelism on the natural transition in boundary layers around underwater axisymmetric bodies. This paper develops the method of harmonic linearized Navier–Stokes (HLNS) equations for such boundary layers. The method can be used to study the evolution of small disturbances considering the non-parallelism of the basic flow. Based on the results of the HLNS equations, the transition positions of the boundary layers are predicted by the eN method. Because traditional linear stability theory (LST) neglects non-parallelism, the difference between the results using the HLNS method and those given by LST represents the effects of non-parallelism. Numerical calculations are performed for five classical forebody shapes, and the effect of non-parallelism is identified. (i) At each streamwise location, non-parallelism suppresses low-frequency disturbances while promoting high-frequency ones. The influence on high-frequency disturbances is more obvious. (ii) The effect of non-parallelism on the neutral curve focuses on the region near the critical instability position. Non-parallelism slightly delays the critical instability position for most forebody shapes and pushes the unstable zone toward the high-frequency direction while broadening its frequency bandwidth. (iii) The streamwise range at each growth-rate level is widened, implying that non-parallelism destabilizes the boundary layers. (iv) The transition occurs earlier, indicating that non-parallelism promotes the transition. (v) Non-parallelism shifts the dangerous frequency band toward the high-frequency direction. (vi) Non-parallelism enhances the wall pressure fluctuations (WPF) in the unstable laminar zone.

A Nomogram for Predicting Late-Onset Neurological Deficits in the Natural Course of Kyphosis or Kyphoscoliosis.

Retrospective single-center comparative analysis. To develop a nomogram model for predicting late-onset neurological deficits (LONDs) in patients with kyphosis or kyphoscoliosis. Patients with kyphosis or kyphoscoliosis might suffer from LONDs, and surgical correction may improve neurological function. Nevertheless, there exists a significant gap in the identification of predictive factors for LONDs in these patients. A consecutive series of 244 patients with kyphosis or kyphoscoliosis who underwent corrective surgery between April 2010 and June 2024 were included in our study. Relevant measurements, including the Cobb angle, deformity angular ratio (DAR), and level of the apex were assessed and calculated using X-ray imaging. Spinal cord morphology at the apex of the major curve was evaluated using preoperative axial T2-weighted magnetic resonance imaging (MRI) to categorize patients into three types based on the spinal cord shape classification system (SCSCS). To identify independent risk factors associated with LONDs, we employed univariate analysis followed by backward stepwise multivariate logistic regression analysis. A nomogram was established based on the identified independent risk factors to predict the likelihood of LONDs in patients with kyphosis or kyphoscoliosis. The mean age of the 244 patients was 46.4±17.8 years, with an observed incidence of LONDs at 57.8%. The backward stepwise multivariate logistic regression analysis indicated that age, etiological diagnosis and SCSCS were independent predictors of LONDs. Utilizing these independent risk factors, we constructed a nomogram model to estimate the probability of LONDs. The concordance index (C-index) of the model was 0.912 (95% CI, 0.876-0.947), indicating a satisfactory level of accuracy in predicting the likelihood of LONDs. The predictive factors for LONDs include age, etiological diagnosis and SCSCS. We developed a nomogram model to predict LONDs, which could be useful for patient counseling and facilitating treatment-related decision-making.

RJAN: Region‐based joint attention network for 3D shape recognition

AbstractAs an essential field of multimedia and computer vision, 3D shape recognition has attracted much research attention in recent years. Multiview‐based approaches have demonstrated their superiority in generating effective 3D shape representations. Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors. However, there exist two disadvantages: First, the mainstream methods ignore the comprehensive exploration of local information in each view. Second, many approaches roughly aggregate multiview features by adding or concatenating them together. The information loss for some discriminative characteristics limits the representation effectiveness. To address these problems, a novel architecture named region‐based joint attention network (RJAN) was proposed. Specifically, the authors first design a hierarchical local information exploration module for view descriptor extraction. The region‐to‐region and channel‐to‐channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning. Subsequently, a novel relation‐aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation, considering the view‐to‐view relationships. Extensive experiments were conducted on three public databases: ModelNet40, ModelNet10, and ShapeNetCore55. RJAN achieves state‐of‐the‐art performance in the tasks of 3D shape classification and 3D shape retrieval, which demonstrates the effectiveness of RJAN. The code has been released on https://github.com/slurrpp/RJAN.

FSH3D: 3D Representation via Fibonacci Spherical Harmonics

AbstractSpherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics.

Neural Vector Fields for Implicit Surface Representation and Inference

AbstractNeural implicit fields have recently shown increasing success in representing, learning and analysis of 3D shapes. Signed distance fields and occupancy fields are still the preferred choice of implicit representations with well-studied properties, despite their restriction to closed surfaces. With neural networks, unsigned distance fields as well as several other variations and training principles have been proposed with the goal to represent all classes of shapes. In this paper, we develop a novel and yet a fundamental representation considering unit vectors in 3D space and call it Vector Field (VF). At each point in $$\mathbb {R}^3$$ R 3 , VF is directed to the closest point on the surface. We theoretically demonstrate that VF can be easily transformed to surface density by computing the flux density. Unlike other standard representations, VF directly encodes an important physical property of the surface, its normal. We further show the advantages of VF representation, in learning open, closed, or multi-layered surfaces. We show that, thanks to the continuity property of the neural optimization with VF, a separate distance field becomes unnecessary for extracting surfaces from the implicit field via Marching Cubes. We compare our method on several datasets including ShapeNet where the proposed new neural implicit field shows superior accuracy in representing any type of shape, outperforming other standard methods. Codes are available at https://github.com/edomel/ImplicitVF.

Classification of Deformable Smooth Shapes Through Geodesic Flows of Diffeomorphisms

Classification of Deformable Smooth Shapes Through Geodesic Flows of Diffeomorphisms

Optimisation of Active Magnetic Elements in Beam-like Structures—Numerical Modelling Studies

This paper explores integrating advanced materials, including magnetic shape memory alloys, magnetorheological fluids, and classical shape memory alloys, within structural elements to achieve exceptional physical properties. When these materials are integrated within structures—whether as wires, actuators, or dampers—they provide the structures with unique static, dynamic, and damping characteristics not commonly found in nature. This study aimed to evaluate the efficacy of these active materials in enhancing the performance of beam-like structures. This investigation was conducted through a comprehensive numerical analysis, focusing on a composite beam. The study examined the impact of different active elements, their position within the structure, and their influence on key dynamic properties. Additionally, a simplified damage scenario was considered, wherein the adverse effects of structural damage were mitigated through the strategic application of these materials. Numerical simulations were carried out using the finite element method, with custom computational codes developed in MATLAB. The findings of these simulations are presented and discussed in this paper.

Soil water repellency under Eucalyptus stands of different age and its relationship with soil hydrological function and soil organic matter

Eucalyptus stands can modify soil hydrological functioning, by several mechanisms including the effect of the growing root system, changes in soil organic matter (SOM) and the occurrence of soil water repellency (SWR). This effect is expected to evolve with time, as tree root system develop and SOM is formed from tree litter. SWR can prevent water from entering the soil, reducing infiltration, while the effect of roots and SOM can improve structure, favoring infiltration. The aims of this study were: i- to determine the influence of Eucalyptus stands of different ages on soil physical and hydraulic properties and SWR phenomena; ii- to evaluate the effects of SOM content and composition on SWR; iii- to determine the relationship between SWR and soil physical properties; iv- to compare different methodologies for SWR evaluation. The soil studied was a in a silty loam Typic Argiudoll with Eucalyptus stands of 3, 7, 11 and 32 years. Samples were taken to determine saturated and near saturation hydraulic conductivity, water retention curve, total organic carbon and different fractions of SOM, and SWR. Different parameters were used to estimate SWR, including the repellency index (RI), water repellency cessation time (WRCT), modified repellency index (RIm), and a qualitative analysis of the shapes of cumulative infiltration curves. As main results, a reduction in total porosity (TP) in the first years of Eucalyptus plantation was found, followed by a steep increased in TP, mainly due to an increase in microporosity. Hydraulic conductivity decreased with stand age, which was attributed to a decrease in macro and mesoporosity and porosity connectivity. SOM and most SOM fractions were not affected by stand age. RI was a consistent method to estimate SWR in Eucalyptus stands, while WRCT and RIm determination was more difficult when non classical shapes of infiltration curves were obtained.

Prevalence, impact and associated factors of abnormal preoperative investigation result in patients undergoing surgery in Dilla University referral hospital: cross-sectional study

Background: As one domain of preoperative assessment, preoperative investigations are often ordered to evaluate patient’s medical condition for risk stratification and assessing patient status to undergoing surgery. Despite the fact that laboratory testing can assist in ensuring the best possible preoperative condition, routine screening examinations have a number of drawbacks. Although there are evidence-based recommendations for which investigations should be done, the tradition of routine preoperative testing is still prevalent and clinical practice with abnormal results detected varies. Method: Institution-based cross-sectional study design was conducted from 1 November to January at Dilla University Referral Hospital. Data was collected from complete pre-anaesthesia check-up sheets, investigations already done. It was collected at the individual level by using, closed-ended self-guided questionnaire. The collected data was entered, cleaned, edited and checked using SPSS version 26 for data processing and analysis. Logistic regression was performed to examine the impacts of abnormal preoperative investigation results and summarised by using tables and figures. An Adjusted odds ratio with 95% CI was computed to determine the level of significance. Result: Data of 208 patients (65.9 female) with mean±standard deviation age 30.83±15.340 years and 22.59±2.99 BMI were analysed. Patients were mostly American Society of Anaesthesiologists I and II underwent National Institute of Clinical and Health Excellence Grade 2 surgeries and surgical shape class 3. Totally, 178 (44.5%) test results were abnormal. CBC is the most detected abnormal result. Only 15 (3.75%) abnormalities had an impact in terms of delay, further investigations, and surgical technique. Comorbidity (AOR 7.982, 95% CI, P=0.041), medication history (AOR 1.463, 95% CI, P=0.013), ASA physical status II (AOR 3.287, 95% CI, P=0.029) and history of smoking (AOR 1.577, 95% CI, P=0.049) were factors which was significantly associated with abnormal preoperative investigation result. Conclusion: Only 0.6% of all tests had a significant impact in terms of changing perioperative anaesthetic management. The significant impact of abnormal investigation result noticed was delayed surgery

Enhancing MeshNet for 3D shape classification with focal and regularization losses

With the development of deep learning and computer vision, an increasing amount of research has focused on applying deep learning models to the recognition and classification of three-dimensional shapes. In classification tasks, differences in sample quantity, feature amount, model complexity, and other aspects among different categories of 3D model data cause significant variations in classification difficulty. However, simple cross-entropy loss is generally used as the loss function, but it is insufficient to address these differences. In this paper, we used MeshNet as the base model and introduced focal loss as a metric for the loss function. Additionally, to prevent deep learning models from developing a preference for specific categories, we incorporated regularization loss. The combined use of focal loss and regularization loss in optimizing the MeshNet model’s loss function resulted in a classification accuracy of up to 92.46%, representing a 0.20% improvement over the original model’s highest accuracy of 92.26%. Furthermore, the average accuracy over the final 50 epochs remained stable at a higher level of 92.01%, reflecting a 0.71% improvement compared to the original MeshNet model’s 91.30%. These results indicate that our method performs better in 3D shape classification task.

Classification of land lot shapes in real estate sector using a convolutional neural network

In the agriculture and real estate industries, land lot shapes have mostly been classified by visual inspection or hard-crafted rules. These conventional methods are time-consuming, resource-intensive, and subject to human bias. This study aims to fill this gap and alleviate problems inherent in traditional lot classification approaches. This study attempts to classify lot shapes automatically, using a convolutional neural network. A study area was chosen, image data of the lots in the area were collected and preprocessed, and an Xception neural network was specified to classify land lots according to their shapes. The test applied to a different area adjacent to the study area achieved an accuracy of 90.1% and area under the curve (AUC) of 0.96. Additionally, this study demonstrated that shape regularity can be quantified using the output scores from the neural network analysis. This is the first attempt to employ a deep learning algorithm for land management on a micro-spatial scale. The classification approach proposed in this study is expected to encourage the rapid and accurate classification of various lot shapes.

Anisakis extracellular vesicles elicit immunomodulatory and potentially tumorigenic outcomes on human intestinal organoids

BackgroundAnisakis spp. are zoonotic nematodes causing mild to severe acute and chronic gastrointestinal infections. Chronic anisakiasis can lead to erosive mucosal ulcers, granulomas and inflammation, potential tumorigenic triggers. How Anisakis exerts its pathogenic potential through extracellular vesicles (EVs) and whether third-stage infective larvae may favor a tumorigenic microenvironment remain unclear.MethodsHere, we investigated the parasite's tumorigenic and immunomodulatory capabilities using comparative transcriptomics, qRT-PCR and protein analysis with multiplex ELISA on human intestinal organoids exposed to Anisakis EVs. Moreover, EVs were characterized in terms of shape, size and concentration using classic TEM, SEM and NTA analyses and advanced interferometric NTA.ResultsAnisakis EVs showed classic shape features and a median average diameter of around 100 nm, according to NTA and iNTA. Moreover, a refractive index of 5–20% of non-water content suggested their effective biological cargo. After treatment of human intestinal organoids with Anisakis EVs, an overall parasitic strategy based on mitigation of the immune and inflammatory response was observed. Anisakis EVs impacted gene expression of main cytokines, cell cycle regulation and protein products. Seven key genes related to cell cycle regulation and apoptosis were differentially expressed in organoids exposed to EVs. In particular, the downregulation of EPHB2 and LEFTY1 and upregulation of NUPR1 genes known to be associated with colorectal cancer were observed, suggesting their involvement in tumorigenic microenvironment. A statistically significant reduction in specific mediators of inflammation and cell-cycle regulation from the polarized epithelium as IL-33R, CD40 and CEACAM1 from the apical chambers and IL-1B, GM-CSF, IL-15 and IL-23 from both chambers were observed.ConclusionsThe results here obtained unravel intestinal epithelium response to Anisakis EVs, impacting host’s anthelminthic strategies and revealing for the first time to our knowledge the host-parasite interactions in the niche environment of an emerging accidental zoonosis. Use of an innovative EV characterization approach may also be useful for study of other helminth EVs, since the knowledge in this field is very limited.Graphical

3D characteristic indicator analysis and comprehensive evaluation of manufactured sand particles used in high-performance concrete

Two-dimensional particle morphology indices face challenges in accurately depicting the characteristics of three-dimensional (3D) manufactured sand (MS), which does not effectively guide the selection of MS for high-performance concrete (HPC). This study aimed to obtain a quantified comprehensive evaluation of actual MS particle groups with specific gradations by thoroughly analysing 3D geometric characteristics. Two representative MS samples were selected for exploration, and river sand (RS) was used as the control. A random selection of sand particles was screened on six sieve levels ranging from 0.15 mm to 4.75 mm. Using X-ray computed tomography and associated image processing technologies, 3D models of these particles were generated, and MATLAB was used to extract their 3D morphology information. Subsequently, the change rules and validity of current 3D particle morphology indicators were examined, focusing on shape, angularity, and texture. Moreover, a comprehensive morphology index, Cm, was proposed by considering the effects of gradation and particle shape classification. The results showed that shape indices more accurately reflected particle characteristics, notably, a higher proportion of spheroid particles, a lower aspect ratio, and greater sphericity correlated with improved particle shape; contrary to traditional beliefs, the angularity index suggested that greater roundness did not necessarily equate to more rounded particles owing to the influence of the proportion of spheroid and cubic particles; meanwhile, texture indices showed minimal difference in the mean values of the 3D fractal dimension for the three types of sand. The proposed index Cm effectively differentiated the particle morphology states of the three sands, with higher Cm values indicating superior particle morphology. The smaller the difference between the Cm values of MS and RS, the better substitute of MS for RS.

On Robust Stabilization of Motion of a Quadrotor with Slung Load

One of the important tasks that can be performed by quadrotors is the transportation of various payloads. If the load is suspended from the quadrotor and can move relative to the quadrotors body, this motion must be taken into account in the control system, in particular, in order to suppress and prevent unwanted oscillations of the load. In the present paper, a mechanical system is considered that consists of a quadrotor and a load suspended to it with a weightless rod. It is assumed that the cross-sectional area of the load is large enough so that the aerodynamic forces acting on it cannot be neglected. It is known that for a number of shapes of payload casing, this aerodynamic force is not reduced only to the drag force, but also contains a component orthogonal to the drag force, that is, the lift force. In order to these forces, the quasi-steady approach is used. At the same time, accurate information about the aerodynamic capabilities for each particular payload is, generally speaking, not available. However, it is known that the values of aerodynamic coefficients for a fairly wide class of body shapes are lie in a specific range. Here we discuss the problem of developing the quadrotor control that would ensure robust stabilization of the ascent and descent of the system in the conditions of incomplete information about the aerodynamic load. A method is proposed for constructing the quadrotor control for robust stabilization of uniform vertical ascent and descent of the system as a whole. It is shown that this control ensures stabilization of the target mode within a fairly wide range of system parameters. Restrictions on the target speed of quadrotor motion are determined, violation of which makes the robust stabilization impossible.

Soft cells and the geometry of seashells.

A central problem of geometry is the tiling of space with simple structures. The classical solutions, such as triangles, squares, and hexagons in the plane and cubes and other polyhedra in three-dimensional space are built with sharp corners and flat faces. However, many tilings in Nature are characterized by shapes with curved edges, nonflat faces, and few, if any, sharp corners. An important question is then to relate prototypical sharp tilings to softer natural shapes. Here, we solve this problem by introducing a new class of shapes, the soft cells, minimizing the number of sharp corners and filling space as soft tilings. We prove that an infinite class of polyhedral tilings can be smoothly deformed into soft tilings and we construct the soft versions of all Dirichlet-Voronoi cells associated with point lattices in two and three dimensions. Remarkably, these ideal soft shapes, born out of geometry, are found abundantly in nature, from cells to shells.

The Determination of Criticality for Ice Shapes Based on CCAR-25

Determining the criticality of ice shapes is a necessary condition for verifying compliance with icing airworthiness regulations. However, the clear, concise, and applicable criterion based on the geometric characteristics of ice shapes has not been clearly given out by current advisory circulars. To address this problem, this paper summarizes aerodynamic performance items and recommended ice shapes the latest version of CCAR-25 and corresponding advisory circulars for a variety of flight phases, including takeoff, holding, en route, DTO, etc., instead of the single phase of holding in the previous research. Based on the geometric classification of the ice shapes, the dominant parameters of various ice shapes are clarified by the correlation between the geometric parameters and aerodynamic effects. The geometric parameters to determine the criticality of specific ice shapes are defined as the roughness height and range for the roughness ice and the total projection height in the direction of lift for the horn ice. On this basis, the detailed determination criterion of critical ice shape geometries corresponding to different flight phases and aircraft components is formulated, which will provide an operational selection methodology for determining the geometries of critical ice shapes at the airworthiness certification stage.

Machine Learning-Based Tomato Fruit Shape Classification System.

Fruit shape significantly impacts the quality and commercial value of tomatoes (Solanum lycopersicum L.). Precise grading is essential to elucidate the genetic basis of fruit shape in breeding programs, cultivar descriptions, and variety registration. Despite this, fruit shape classification is still primarily based on subjective visual inspection, leading to time-consuming and labor-intensive processes prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool and considering the current four classification systems as label variables. Subsequently, based on class-specific metrics, we derived a novel classification framework comprising seven discernible shape classes. The results demonstrate the superiority of the Support Vector Machine model in terms of its accuracy, surpassing human classifiers across all classification systems. The new classification system achieved the highest accuracy, averaging 88%, and maintained a similar performance when validated with an independent dataset. Positioned as a common standard, this system contributes to standardizing tomato fruit shape classification, enhancing accuracy, and promoting consensus among researchers. Its implementation will serve as a valuable tool for overcoming bias in visual classification, thereby fostering a deeper understanding of consumer preferences and facilitating genetic studies on fruit shape morphometry.

Graph Transformer for 3D point clouds classification and semantic segmentation

Recently, graph-based and Transformer-based deep learning have demonstrated excellent performances on various point cloud tasks. Most of the existing graph-based methods rely on static graph, which take a fixed input to establish graph relations. Moreover, many graph-based methods apply maximizing and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points own the same influence on the centroid’s feature, which ignoring the correlation and difference between points. Most Transformer-based approaches extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the above issues of graph-based and Transformer-based models, we propose a new feature extraction block named Graph Transformer and construct a 3D point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer that use intra-domain cross-attention and Global Transformer that use global self-attention. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper. The experimental results show that our model achieves good learning and prediction ability on most tasks. The source code and pre-trained model of GTNet will be released on https://github.com/NWUzhouwei/GTNet.

HQ-Net: A heatmap-based query backbone for point cloud understanding

With the advancement of sensor technology, including LiDAR and RGB-D cameras, tasks related to point cloud understanding have gained widespread application. The sampling and learning of feature points play a crucial role in point cloud understanding, representing a fundamental and essential aspect of 3D computer vision. In the realm of point cloud processing, traditional methods such as Farthest Point Sampling (FPS) are frequently employed to select feature points. These points are then used in conjunction with effective feature aggregation operators to facilitate reasoning. However, these traditional sampling methods primarily focus on the Euclidean distance between points, lacking a comprehensive consideration of point cloud features. To address this limitation, we introduce HQ-Net, a novel point cloud understanding model designed to optimize feature point sampling using a Heatmap-based Query Backbone. The Heatmap-based Query Backbone utilizes the Heatmap Module to select category-specific point features, effectively generating a heatmap tailored to the category features and automatically extracting features from the heatmap. Subsequently, the proposed Query Encoder is employed to facilitate the interaction between point features and query features, providing mutual guidance and enhancing the model’s ability to capture spatial structure and semantic information within point clouds. Additionally, we propose task-specific heads, such as classification and segmentation heads for different tasks, which can be seamlessly integrated with the backbone to achieve excellent performance. In various point cloud understanding tasks, including shape classification, part segmentation, and semantic segmentation, HQ-Net demonstrates excellent performance.