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.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Axis Problem of Rough 3-Valued Algebras

The collection of all the rough sets of an approximation space has been given several algebraic interpretations, including Stone algebras, regular double Stone algebras, semi-simple Nelson algebras, pre-rough algebras and 3-valued Lukasiewicz algebras. A 3-valued Lukasiewicz algebra is a Stone algebra, a regular double Stone algebra, a semi-simple Nelson algebra, a pre-rough algebra. Thus, we call the algebra constructed by the collection of rough sets of an approximation space a rough 3-valued Lukasiewicz algebra.In this paper,the rough 3-valued Lukasiewicz algebras, which are a special kind of 3-valued Lukasiewicz algebras, are studied. Whether the rough 3-valued Lukasiewicz algebra is a axled 3-valued Lukasiewicz algebra is examined.

基于E^3-Value的服务供应链运作管理流程和方法

目前尽管存在较多以产品为中心的供应链运作管理流程和方法,但在服务供应链领域,由于服务不同于产品的诸多特性局限了其进一步的应用。因此,采用结构化分析思想和方法,提出了基于E3-value的服务供应链运作管理逻辑流程和方法,该方法以价值为导向,不但可以解决现有供应链运作管理模式的一些适应性问题,而且能够解决"供应链运作管理模式为什么是这样,潜在盈利性如何量化"问题。

基于e^3-value的移动商务商业模式仿真分析

本文运用e3-value结构化仿真分析方法构建基于价值链的移动商务商业模式的价值模型。通过相关参数输入模型,对其进行模拟试算,得到该商业模式的盈利性和合理性评价。通过e3-value模型创新关键因素价值活动、价值界面及价值端口的解构,调整价值模型及其变量,得到移动商务价值网商业模式价值模型。通过模型试算结果对比,分析讨论移动商务价值网商业模式的合理性及模型创新发展策略。

基于e^3-value的电子商务模式的结构化分析——以数据库服务提供商为例

在对电子商务模式的描述方法研究进行综述的基础上,介绍了e3-value的主要概念及分析步骤,并以数据库服务提供商的电子商务模式为例,说明了利用e3-value进行结构化分析的基本过程,最后对该分析方法进行了评价。

基于e3-value模型的iPhone APP Store价值网络分析

文章以i Phone APP Store为例,在运用VNA模型分析价值网络结构的基础上,建立i Phone APP Store的e3-value模型,根据参与者盈利能力表和财务敏感性分析获得战略制定的相关依据因素。

Semantic segmentation method of road scene based on Deeplabv3+ and attention mechanism

In the study of automatic driving,understanding the road scene is a key to improve driving safety.The semantic segmentation method could divide the image into different areas associated with semantic categories in accordance with the pixel level,so as to help vehicles to perceive and obtain the surrounding road environment information,which would improve driving safety.Deeplabv3+is the current popular semantic segmentation model.There are phenomena that small targets are missed and similar objects are easily misjudged during its semantic segmentation tasks,which leads to rough segmentation boundary and reduces semantic accuracy.This study focuses on the issue,based on the Deeplabv3+network structure and combined with the attention mechanism,to increase the weight of the segmentation area,and then proposes an improved Deeplabv3+fusion attention mechanism for road scene semantic segmentation method.First,a group of parallel position attention module and channel attention module are introduced on the Deeplabv3+encoding end to capture more spatial context information and high-level semantic information.Then,an attention mechanism is introduced to restore the spatial detail information,and the data shall be normalized in order to accelerate the convergence speed of the model at the decoding end.The effects of model segmentation with different attention-introducing mechanisms are compared and tested on CamVid and Cityscapes datasets.The experimental results show that the mean Intersection over Unons of the improved model segmentation accuracies on the two datasets are boosted by 6.88%and 2.58%,respectively,which is better than using Deeplabv3+.This method does not significantly increase the amount of network calculation and complexity,and has a good balance of speed and accuracy.

基于e^3-value的现实版“开心农场”商业模式创新途径分析

现实版"开心农场"因过于简单的商业模式而陷入经营困境。本文借助成熟的e^3-value分析方法,结合浙江PS开心农场的具体案例分析其面临的经营困境并探讨创新的商业模式,结果表明以价值网理念创新的商业模式具有一定的生存性与盈利性。

Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation.Segmentation is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent organization.The research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and algorithms.Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I.methods.However,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial stages.This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation.In these approaches’benefits,draw backs,and design mechanisms are studied and addressed.This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

基于E3-Value的网络团购商业模式的分析与优化:价值网协同创新的视角

网络团购(on-line group shopping)作为一种新兴和重要的商业模式B2T (Business to Team)近年来发展迅速,同时又呈现出一些不确定性。这一商业模式的价值主张或价值体现(value proposition)在于通过集合订单使消费者获取最佳的价格(find the best price),同时为消费者带来了特殊的消费和服务体验,其本质是一种折扣交易(discount deals)。但国内这一模式的发展普遍存在同质化、易于模仿和缺乏可持续的盈利模式等问题而遭遇发展瓶颈。文章采用e3-value系统分析方法和工具,对网络团购商业模式的价值本体与价值活动进行了结构化分析;针对其缺乏行业协同性问题,基于价值网这一价值创造和协同的“软集成”模式,提出了一种新的商业模式——团购价值网;进一步的价值分析表明,这一新的商业模式不仅能通过构建共生共赢的价值网生态系统形成新的价值共同体,还有利于提高各参与方的收益。最后从管理的角度,提出了一些对策与建议,以实现网络团购商业模式的重组与优化,提升这一行业的竞争力与生命力。

ЭТАП-3机器翻译系统研究

通过对ЭТАП-3机器翻译系统的简要概述,重点对相关模块、特征及句法语义处理方法进行分析,目的在于借鉴该机器翻译系统先进的句法语义处理方法,尝试建立可用于俄汉机器翻译系统的应用模块,从而改善现有俄汉机器翻译系统的性能。

Linking a Game Engine Environment to Architectural Information on the Semantic Web

Because of the importance of graphics and information within the domain of architecture, engineering and construction （AEC）, an appropriate combination of visualization technology and information management technology is of utter importance in the development of appropriately supporting design and construction applications. Virtual environments, however, tend not to make this information available. The sparse number of applications that present additional information furthermore tend to limit their scope to pure construction information and do not incorporate information from loosely related knowledge domains, such as cultural heritage or architectural history information. We therefore started an investigation of two of the newest developments in these domains, namely game engine technology and semantic web technology. This paper documents part of this research, containing a review and comparison of the most prominent game engines and documenting our architectural semantic web. A short test-case illustrates how both can be combined to enhance information visualization for architectural design and construction.

卢卡锡维茨Ł_(3)系统的最低限度隐变量解释

一般认为卢卡锡维茨Ł_(3)系统中的排中律与(不)矛盾律都不成立,对此有许多不同解释。按照STRF理论的简化、等值与增力的“三合一”极致原则,在完全接受Ł_(3)句法和维持其已有语义的前提下,更深层次上“第三真值”这个逻辑常量其实是一个严格等价隐变量,也即“1/2”真其实是“真与并非真”的一种组合,但同一组合有不同排列,甚至同一个真值度存在不同的真值分布。显然由此可计算出Ł_(3)中的排中律与(不)矛盾律仍然都是定理,Ł_(3)当然是完全的,不过就是没被彻底认识的经典命题逻辑系统CP。

Study on 3D Geological Model of Highway Tunnels Modeling Method

Geology is the base for highways and tunnels construction. With the fast development of national highway construction, highway tunnel construction project are more and more complex. The completeness and accuracy are essential for the planning, design and construction of projects, while the ground information is quite poor in systematic, reliable and timely aspects. Therefore, the development of underground road tunnels, and the implementation of informationized spatial information management is urgent for highway construction. 3D geological tunnel model is intuitive, high efficient and convenience which greatly facilitates the maintenance and security of highway tunnels construction and it will be the trend for the future highway tunnel development.

Next Generation Semantic and Spatial Joint Perception——Neural Metric-Semantic Understanding

Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario.