A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

无人机载LaBr_(3)(Ce)探测器对点源的最小可探测活度研究

随着核能与核技术应用的不断发展,对无人机载航空测量设备提出了新的要求。LaBr_(3)(Ce)探测器以其优良的能量分辨率(<3%@661.2 keV)以及较高的探测效率受到了特别的关注。本文通过开展对LaBr_(3)(Ce)探测器的性能测试及无人机载飞行实验,对2″×φ2″英寸LaBr_(3)(Ce)探测器在不同条件下对点源的探测能力进行了研究。测试内容主要包括对LaBr_(3)(Ce)探测器的本底测试、探测效率测试、角响应测试等。飞行实验包括在不同辐射本底下的不同高度、不同速度、不同偏航距的飞行测试。确定了2″×φ2″英寸LaBr_(3)(Ce)探测器在K为557.01 Bq/kg、U为17.64 Bq/kg、Th为29.41 Bq/kg的辐射背景场下,不同飞行条件时探测器对~(137)Cs点源的最小可探测活度。

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

This article describes a novel approach for enhancing the three-dimensional(3D)point cloud reconstruction for light field microscopy(LFM)using U-net architecture-based fully convolutional neural network(CNN).Since the directional view of the LFM is limited,noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds.The existing methods suffer from these problems due to the self-occlusion of the model.This manuscript proposes a deep fusion learning(DL)method that combines a 3D CNN with a U-Net-based model as a feature extractor.The sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing.A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature fromthe directional sub-aperture LF data cube.For the enhancement of the depthmap,dual iteration-based weighted median filtering(WMF)is used to reduce surface noise and enhance the accuracy of the reconstruction.Generating a 3D point cloud involves combining two key elements:the enhanced depth map and the central view of the light field image.The proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing(HCI)and real-world LFM datasets.The results are compared with different state-of-the-art methods.The structural similarity index(SSIM)gain for boxes,cotton,pillow,and pens are 0.9760,0.9806,0.9940,and 0.9907,respectively.Moreover,the discrete entropy(DE)value for LFM depth maps exhibited better performance than other existing methods.

Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.

低压环境下RP-3航空煤油自燃特性研究

开展了低压环境下RP-3航空煤油的自燃特性研究,测试了101 kPa、80 kPa、60 kPa、40 kPa、20 kPa五种压力环境下航空煤油自燃点及着火延迟时间数据。结果表明,随着环境压力降低,自燃点升高,并基于谢苗诺夫热自燃理论,建立了耦合压力参数的自燃点预测模型。分析了环境压力对着火延迟时间的影响机制,建立了不同环境压力下的着火延迟时间预测模型。分析了GB/T 5332—2007标准在低压环境下测试存在的问题,并提出了相关改进方法。

BDS-3 PPP-B2b实时精密时间同步方法探讨

针对传统实时精密单点定位(PPP)时间同步方法需要连接互联网,以及对北斗三号全球卫星导航系统(BDS-3)精密单点定位服务信号(PPP-B2b)用于长基线实时时间同步的研究较少的问题,提出一种将PPP-B2b信号用于PPP时间同步的实时时间同步方法:将高精度铷原子钟作为全球卫星导航系统(GNSS)接收机外频标输入,通过GNSS接收机实时接收PPP-B2b改正数,解算接收机钟差并实时调整铷原子钟,输出与时间基准同步的秒脉冲(1PPS)信号,较之传统GNSS接收机输出1PPS有更佳的频率稳定性;设计PPP-B2b时间同步硬件测试平台对提出的PPP-B2b时间同步方法进行测试。结果表明,通过PPP-B2b时间同步方法进行时间传递的精度优于0.2 ns;短基线实测实时时间同步精度优于0.95 ns;卫星模拟器仿短基线精度优于0.87 ns,仿长基线精度优于0.9 ns。

BDS-3 PPP模糊度固定实现及精度评估

精密单点定位(PPP)模糊度固定(AR)能够显著提升精密定位的收敛速度和精度。通过在BDS-2和BDS-3之间添加系统间偏差的方法实现BDS-3的模糊度固定,并基于全球MGEX测站静态、仿动态数据和车载实验数据全面评估了BDS-3模糊度固定的效果。结果表明,相对于浮点解,BDS-3 PPP模糊度固定能够显著提升PPP的精度,在东北天3个方向上静态解算精度提升依次为37.4%、26.2%和20.1%;仿动态解算精度提升依次为38.3%、27.2%和11.1%;车载动态实验BDS-3模糊度固定精度在三维方向上综合提升为40.4%。此外,模糊度固定后,以浮点解稳定后的两倍定位精度为基准,在东北天方向上,静态定位时间提升程度依次为63.5%、64.0%和40.3%;仿动态定位时间提升程度依次为58.7%、56.8%和25.4%;车载实验在三维方向的收敛时间为30.0 min。以上结果证明了所提方法的有效性及BDS-3模糊度固定的性能提升。

Effect of acupuncture on acupoint "Yingxiang-Hegu" on Th1, Th2 cytokines and T-bet/GATA-3 of allergic rhinitis rats

Objective:To explore the effect of"Yingxiang-Hegu"on Th1,Th2-related cytokines and[2]transcription factors T-bet and GATA-3 in rats with allergic rhinitis.Methods:Rats were randomly divided into three groups:blank group,model group and acupoint group.The rat model of ovalbumin(OVA)AR was established,and the general condition of the rats was observed and scored.Acupuncture intervention was performed on the acupoint group on the second day after successful modeling,once per day for 20 min for 10 d.After intervention,the general behavior,behavioral score and histomorphological changes of nasal mucosa were observed.Eosinophils(EOS)were counted under microscope after nasal lavage smear staining,and the contents of total IgE,IFN-γ,IL-12,IL-4 and IL-5 in serum were detected by ELISA.Westernblot and IHC were used to detect the protein level and positive protein expression of specific transcription factors T-bet and GATA-3 in rat nasal mucosa.Results:After the establishment of the model,except for the blank group,the behavioral observation scores of rats in the model group and acupoint group were more than 5 points,indicating that the model was successful.After acupuncture intervention on acupoint"Yingxiang-Hegu",the behavioral score of rats in the acupoint group and western medicine group was significantly lower than that in the model group(P<0.05).Microscopic examination showed that the structure of nasal mucosa in the model group was obviously damaged,cilia were arranged discontinuously,uneven,local congestion and swelling,a large number of epithelial cells exfoliated and necrotic,goblet cell proliferation,obvious inflammatory cell infiltration.The pathological degree of nasal mucosa in the pair point group was significantly less than that in the model group.Compared with the model group,the levels of IFN-γ,IL-2 and IL-12 in serum were significantly increased,while IgE,IL-4,IL-5 and IL-6 were significantly decreased,GATA-3 protein and positive expression in nasal mucosa were significantly decreased and T-bet was significantly increased after acupuncture.Conclusion:Acupuncture at"Yingxiang-Hegu"can effectively improve the nasal sensitive symptoms and control nasal inflammation in AR rats.The mechanism may be that acupuncture at Yingxiang-Hegu can up-regulate the expression of T-bet,decrease the level of GATA-3,promote the production of Th1 cytokines and inhibit the synthesis and secretion of Th2 cells,thus restoring the immune balance of Th1 and Th2.

国产SW26010-Pro处理器上3级BLAS函数众核并行优化

BLAS(basic linear algebra subprograms)是最基本、最重要的底层数学库之一.在一个标准的BLAS库中,BLAS 3级函数涵盖的矩阵-矩阵运算尤为重要,在许多大规模科学与工程计算应用中被广泛调用.另外,BLAS 3级属于计算密集型函数,对充分发挥处理器的计算性能有至关重要的作用.针对国产SW26010-Pro处理器研究BLAS 3级函数的众核并行优化技术.具体而言,根据SW26010-Pro的存储层次结构,设计多级分块算法,挖掘矩阵运算的并行性.在此基础上,基于远程内存访问(remote memory access,RMA)机制设计数据共享策略,提高从核间的数据传输效率.进一步地,采用三缓冲、参数调优等方法对算法进行全面优化,隐藏直接内存访问(direct memory access,DMA)访存开销和RMA通信开销.此外,利用SW26010-Pro的两条硬件流水线和若干向量化计算/访存指令,还对BLAS 3级函数的矩阵-矩阵乘法、矩阵方程组求解、矩阵转置操作等若干运算进行手工汇编优化,提高了函数的浮点计算效率.实验结果显示,所提出的并行优化技术在SW26010-Pro处理器上为BLAS 3级函数带来了明显的性能提升,单核组BLAS 3级函数的浮点计算性能最高可达峰值性能的92%,多核组BLAS 3级函数的浮点计算性能最高可达峰值性能的88%.

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

不同纬度GPS/BDS-3精密单点定位性能分析

为分析不同纬度区域GPS/BDS-3组合精密单点定位精度及其收敛时间,实验选取了MGEX(Multi-GNSS experiment)7个测站连续14 d的观测数据,对GPS/BDS-3组合进行精密单点定位(PPP)技术定位性能分析,并与GPS、BDS-3单系统进行对比。结果表明,无论是静态定位还是动态定位模式下,由于低纬度地区电离层较为活跃,导致数据质量较差,测站的收敛时间比高纬度测站长。此外,在静态定位模式下,GPS/BDS-3组合在水平方向定位精度优于0.8 cm,高程方向优于1.4 cm;GPS/BDS-3组合对于BDS-3单系统而言在水平方向定位精度提高了26.3%,高程方向提高了42.9%。在动态定位模式下,GPS/BDS-3组合在水平方向定位精度优于1.8 cm,高程方向优于3.8 cm;GPS/BDS-3组合对于BDS-3单系统而言在水平方向定位精度提高了35.59%,高程方向提高了56.10%。

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

AHermitian C^(2) Differential Reproducing Kernel Interpolation Meshless Method for the 3D Microstructure-Dependent Static Flexural Analysis of Simply Supported and Functionally Graded Microplates

This work develops a Hermitian C^(2) differential reproducing kernel interpolation meshless(DRKIM)method within the consistent couple stress theory(CCST)framework to study the three-dimensional(3D)microstructuredependent static flexural behavior of a functionally graded(FG)microplate subjected to mechanical loads and placed under full simple supports.In the formulation,we select the transverse stress and displacement components and their first-and second-order derivatives as primary variables.Then,we set up the differential reproducing conditions(DRCs)to obtain the shape functions of the Hermitian C^(2) differential reproducing kernel(DRK)interpolant’s derivatives without using direct differentiation.The interpolant’s shape function is combined with a primitive function that possesses Kronecker delta properties and an enrichment function that constituents DRCs.As a result,the primary variables and their first-and second-order derivatives satisfy the nodal interpolation properties.Subsequently,incorporating ourHermitianC^(2)DRKinterpolant intothe strong formof the3DCCST,we develop a DRKIM method to analyze the FG microplate’s 3D microstructure-dependent static flexural behavior.The Hermitian C^(2) DRKIM method is confirmed to be accurate and fast in its convergence rate by comparing the solutions it produces with the relevant 3D solutions available in the literature.Finally,the impact of essential factors on the transverse stresses,in-plane stresses,displacements,and couple stresses that are induced in the loaded microplate is examined.These factors include the length-to-thickness ratio,the material length-scale parameter,and the inhomogeneity index,which appear to be significant.

附加先验信息约束的BDS-3精密授时算法

考虑到高性能原子钟的高稳定性及对应站钟钟差序列历元间强相关等特点,对接收机钟差附加先验信息约束进行精密单点定位授时测试。基于BDS-3的B1C/B2b观测信息测试结果表明,相比于传统授时模型,附加先验信息的精密授时模型具有更高的授时性能,尤其是短期稳定性提升效果可达60%。此外,BDS-3的B1I/B3I和B1C/B2b组合观测值PPP精度基本相当,均可获得水平方向优于0.5 cm、高程方向约2 cm的定位精度;得益于B1C/B2b更高的观测数据质量,其定位精度较B1I/B3I组合提高13.8%。

The Collinear Libration Points in the Elliptic R3BP with a Triaxial Primary and an Oblate Secondary

This paper examines the motion of a dust grain around a triaxial primary and an oblate companion orbiting each other in elliptic orbits about their common barycenter in the neighborhood of collinear libration points. The positions and stability of these points are found to be affected by the triaxiality and oblateness of the primaries, and by the semi-major axis and eccentricity of their orbits. The stability behavior of the collinear points however remains unchanged;they are unstable in the Lyapunov sense.