自适应分块的BIM墙体轮廓提取及三维重建研究

建筑信息模型(building information modeling,BIM)技术的发展被视为建筑行业的第二次革命,BIM应用的第一步就是搭建3D建筑信息模型。然而手工搭建BIM模型的效率低下,因此结合Auto CAD及Revit二次开发技术,实现基于建筑平面图的三维快速重建功能。在重建过程中,首先对建筑平面图中的数据进行提取,对重要建筑构件识别进行研究,即对直线墙体、门窗进行识别。在此基础上,利用数据分块思想,提出了自适应分块的墙体轮廓提取算法,解决了墙线段断开的情况,有效地提取到墙体的轮廓及其中的坐标点,从而实现了墙体、门窗的三维重建,提高了BIM建模效率。     

室内平面图分块矢量化与高效三维建筑建模

针对建筑平面图的栅格图像,提出了一种全自动生成三维建筑模型的轻量计算方法。应用平均积分投影函数(integral projection function,IPF)方法对光栅图像进行区域分块,有效地抽取包含墙体对象的子区域。改进了基于非细化的稀疏点像素矢量化(sparse pixel vectorization,SPV)方法,用于抽取墙体的位置和尺寸等信息。为识别出墙体上的门窗和孔洞等建筑部件,将问题转化为图像多分类问题进行求解,同时设计了高效计算方案,精确地定位建筑部件在图纸中的位置。基于识别结果,进行三维建筑模型的快速生成,并集成于笔者所开发的三维建筑快速建模原型系统,方便了三维数据的加工和处理。通过大量实例,验证了所述方法的性能和效率。该方法可用于数字城市、虚拟现实内容创作和公共安全等领域。     

基于特征匹配的建筑平面图自动识别的研究与实现

建筑工程图的计算机识别和理解技术是计算机应用于工程方面的研究热点之一。本文基于建筑工程矢量图形文件(DXF文件),研究了建筑平面图中建筑符号的特征,实现了通过特征匹配的方法自动识别图中的建筑符号及其相互关系。     

语义制导的建筑结构图的全局识别方法

建筑结构图CSD的语法识别方法存在对噪声和差错的敏感性强以及通用性较差等局限,本文对此提出了两点改进方法。一方面,CSD中图形符号之间的关系得到了充分的重视。相互关联的符号分为核心符号和导出符号两类,彼此间关系紧密的所有核心符号被结构化为一个全局符号加以识别。导出符号的识别以核心符号为基础,另一方面,用图形符号在建筑结构领域背景下的语义控制和指导核心符号与导出符号的识别。     

基于图形理解的建筑结构三维重建技术

在实际应用中,现有的三维重建算法无法有效地应用到建筑领域.以建筑行业的基本制图规范为指导,结合图形识别理解和人工智能技术,提出了一种基于图形理解的建筑结构三视图自动识别与重建方法.该方法以自动识别建筑构件的轮廓为基础,通过对符号、注释等语义信息的理解与综合以及对相邻实体间拓扑关系的分析,快捷、有效地完成对建筑物的整体重建.实验结果表明,在少量人机交互的基础上,该方法对实际的建筑图是有效的.     

基于形态图表示的三维物体识别算法

基于形态图表示的三维物体识别的基本思路是：首先建立待识别物体的模型库，找出模型集中所有模型物体的形态图和特征视图，并提取以它们的拓扑结构信息和几何信息；其次对物体真实图像作轮廓提取和0边界跟踪，得到二维图像的线架图，同时提取出它的拓扑结构信息和几何信息；最后将物体图像的拓扑结构信息和几何信息与模型库中模型物体的拓扑结构信息和几何信息匹配，从而达到识别的目的。文中提出了在生成线图链码时提取其拓扑结构信息和几何信息的方法，由拓扑结构信息和几何信息构造特征矩阵的方法，以及识别过程中特征矩阵的匹配算法。     

三维建筑物重建中基于图像的非完全拓扑分析

在计算机视觉和计算机图形学中,单视图三维重建是一个很难的问题。为实现基于单幅图像的快速建筑物识别过程,提出了一种可行的、有效的三维重建方法—非完全拓扑分析。首先,对可见部分的轮廓进行分析并提取建筑形象,得到完整的拓扑结构。其次,将分割后的拓扑结构与原始数据库信息进行快速匹配。实验表明,本方法与SSDA图像匹配算法相比,大大提高了计算速度。     

搜索建筑平面图房间的算法及应用

首先介绍了利用边与边之间的邻接拓扑关系构造邻接矩阵的方法 ,并提出了一种搜索建筑平面图房间的算法 ,可以快速准确地确定平面图内的房间、墙线与顶点的拓扑关系 ,同时 ,给出了实现的方法及应用     

建筑工程图识别与理解--模型与算法

建筑工程图采用二维示意式表达方式,识别和三维精确还原相对困难、提出了一种基于轴网、结构语义驱动的层次式自生长识别模型(SINEHIR模型),在融合结构语义的基础上从先导建筑构件开始,在削减后的识别空间中结合当前识别层次的特点,分别采用基于特征的建筑符号识别方法、基于符号的轴网识别方法、基于轮廓的结点构件识别方法、基于关联语义的段构件识别方法、基于几何的组合构件识别方法和基于继承的信息传递方法,在模型约束下完成后继识别,以较小的计算量有效排除绘图噪音、提高识别率．     

建筑楼板结构平面图的自动识别方法

从三个不同角度提出了分层次、分步骤的建筑楼板结构平面图识别方法．通过消除图元表示方法的多样性、提取特殊图元轮廓信息、建立线条间的几何关系以及基于统计的阈值选取等方法进行规范化处理;通过信息继承实现前导结构图识别结果的重用;通过将梁和墙统一表示成梁段这一辅助数据结构而简化板构件的定义和识别;通过对复制和分布这两种板配筋语义的分析,提出了基于可扩充规则的配筋语义分析方法．该方法已应用于建筑工程算量软件VHRecQS中．     

基于GPU与CPU协作的实时波束形成实现方法*

波束形成的实时性一直是声纳和雷达等领域信号处理过程中的重点和难点。本文采用基于CUDA(Compute Unified Device Architecture,统一计算设备架构)的GPU(Graphic Processing Unit,图形处理器)与CPU协作处理方法,实现了宽带波束形成的实时处理。本方法的处理速度相较于matlab和CPU平台可以提高一至两个数量级,相较于同等处理速度的多DSP平台则体现了开发周期短、费用低、工作量小和可靠性高等众多优势。     

支持建筑墙体造型创新设计的方法研究*

进一步研究了建筑墙体造型的自动化设计,提出了建筑墙体设计的新方法,该方法通过线性不等式围成的凸空间来定义墙体设计的初始元,限制初始元所在的空间为以坐标原点为中心的一定大小的包围盒,并定义这些初始元的空间关系。鉴于建筑墙的特殊性,空间关系限制为相交关系,这里的相交包含相接。对空间关系的定义采取两种方式,一是由用户来手动设置,二是由计算机随机设置,采用向量空间定位法来判断空间关系的有效性。如果空间关系有效,则迭代执行两个形状衍生规则,计算得到不同的造型,空间关系不同衍生代数不同得到的造型也就不同。该方法实现了建筑墙体设计的自动化和智能化,从而达到了辅助设计的目的     

基于形式语法的建筑设计细胞衍生法研究

目前可用的计算机辅助设计工具,为设计开发过程后期阶段提供了相当有效地支 持,但对于设计初始阶段,即设计思路成熟、但设计的结构和形状还未确定之时,需要一种新 的CAD 解决方案来支持设计者思维方式并激发创作灵感。因此,提出一种基于空间形式语法的 建筑设计生成方法,描述复杂空间形体如何自动派生--通过限定的规则产生初始体块,在规 则内生成与初始体块相似体块,再而改变局部可变规则生成多样复杂的系统,同时允许设计者 积极参与整个生成过程。以CFS 墙体原型建模为例,验证该参数化建模方法的有效性和可行性。     

Interactive Iconized Grammar-Based Pailou Modelling

Pailous are representative Chinese architectural works used for commemoration. However, their geometric structure and semantic construction rules are too complex for quick and intuitive modelling using traditional modelling tools. We propose an intuitive modelling system for the stylized creation of pailous for novices. Our system encapsulates structural components as icons and semantic layouts as topological graphs, using which users create and manipulate icons with topological recommendations. The interpreter automatically and immediately transforms a graph to its corresponding model using built-in components with the proposed parametric L-system grammars derived from architectural rules. Using this system to re-create existing representative pailous and design imaginary ones yields results with the desired visual complexities. In comparison to Maya, a 3D modelling tool, when modelling a pailou and toukung, our system is effective and simple, and eliminates the need to remember and understand complex rules.     

Intelligent design of shear wall layout based on graph neural networks

Structural scheme design of shear wall structures is important because it is the first stage that guides the project along its entire structural design process and significantly impacts the subsequent design stages. Design methods for shear wall layouts based on deep generative algorithms have been proposed and achieved some success. However, current generative algorithms rely on pixel images to design shear wall layouts, which have many model parameters and require intensive calculations. Moreover, it is challenging to use pixel image-based methods to reflect the topological characteristics of structures and connect them with the subsequent design stages. The above defects can be effectively solved by representing a shear wall structure in graph data form and adopting graph neural networks (GNNs), which have a robust topological-characteristic-extraction capability. However, there is no existing research using GNN methods in the design of shear wall structures owing to the lack of graph representation methods and high-quality structural graph data for shear walls. Therefore, this study develops an intelligent design method for shear wall layouts based on GNNs. Two graph representation methods for a shear wall structure—graph edge representation and graph node representation—are examined. A data augmentation method for shear wall structures in graph data form is established to enhance the universality of the GNN performance. An evaluation method for both graph representation methods is developed. Case studies show that the shear wall layout designed using the established GNN method is highly similar to the design by experienced engineers.     

Fast construction of plant architectural models based on substructure decomposition

Plant structure,representing the physical link among different organs,includes many similar substructures.In this paper,a new method is presented to construct plant architectural models of most plant species.The plant structure is decomposed into a stem,a set of lateral substructures and a terminal substructure,which is called substructure decomposition;then based on substructure decomposition,the plant structures are expressed in an iterative way;and further the derivative formula is employed to compute the number of organs in plant structures to get the geometrical sizes of 3D plant organs by borrowing Hydraulic Model.Using 3D organs,a substructure library is built.Based on the substructures stored in substructure library,one can construct 3D plant structure according to certain topological and geometrical rules.The experiments with different plant species are included in this paper to demonstrate the validity of the new method for constructing plant structures.The experimental results show that the approach follows botanical knowledge with high efficiency in constructing plant structures of most plant species.In addition,this method enables users to check the detail information of plant structure.     

Consistency constraints and 3D building reconstruction

Virtual architectural (indoor) scenes are often modeled in 3D for various types of simulation systems. For instance, some authors propose methods dedicated to lighting, heat transfer, acoustic or radio-wave propagation simulations. These methods rely in most cases on a volumetric representation of the environment, with adjacency and incidence relationships. Unfortunately, many buildings data are only given by 2D plans and the 3D needs varies from one application to another. To face these problems, we propose a formal representation of consistency constraints dedicated to building interiors and associated with a topological model. We show that such a representation can be used for: (i) reconstructing 3D models from 2D architectural plans (ii) detecting automatically geometrical, topological and semantical inconsistencies (iii) designing automatic and semi-automatic operations to correct and enrich a 2D plan. All our constraints are homogeneously defined in 2D and 3D, implemented with generalized maps and used in modeling operations. We explain how this model can be successfully used for lighting and radio-wave propagation simulations.     

Combining topological and view-based features for 3D model retrieval

With the rapidly increasing of 3D models, the 3D model retrieval methods have been paid significant research attention. Most of the existing methods focus on taking advantage of one kind of feature. These methods can not achieve ideal retrieval results for different classes of 3D models. In this paper, we propose a novel 3D model retrieval algorithm by combining topological and view-based features. To preserve the topological structure of the 3D model, a multiresolutional reeb graph (MRG) is constructed according to the salient topological points. The view-based features are extracted from the images, which are rendered at each of the topological points. To preserve the spatial structure information of the images, we modify the bag-of-features (BOF) method by using the combined shell-sector model. We take the view-based features as the attribute information of the corresponding MRG nodes. The comparison between two 3D models is transformed to the problem of computing the similarity of the corresponding MRGs. Finally, we calculate the similarity between the query model and the models in the databases by adapting the earth mover distance method. Experimental results on two standard benchmarks show that our algorithm can achieve satisfactory retrieval performance.