Structure simplification of dynamic process models

Lumped process models derived from first engineering principles are usually too detailed for control purposes where only the major dynamic characteristics of the system should be captured. Two common steps of simplifying dynamic process models, the steady-state variable removal and the variable lumping simplification steps are investigated in this paper, in order to show if they preserve the key properties: the structural controllability, observability and stability of the models. In order to enable the formal analysis, these simplification steps are represented as context sensitive graph transformations acting on the structure graphs of the dynamic process models. It is shown that the simplification transformations above preserve the structural controllability and observability of process models. But only the steady-state variable removal transformation has been found not to destroy their structural stability. The variable lumping structure simplification transformation is further specialized to the case of cascade process models. It is shown that the inverse of this transformation does exist in this case, and both transformations preserve structural controllability and observability.     

An efficient solder joint defects method for 3D point clouds with double-flow region attention network

In electronics mass-production, image-based methods are often used to detect the solder joint defects for achieving high-quality assurance with low labor costs. Recently, deep learning in 3D point clouds has shown an effective form of characterization for 3D objects. However, existing work rarely involves defect detection for PCBs based on 3D point clouds. In this paper, we propose a novel neural network named double-flow region attention network (DoubRAN) to detect defects of solder joints with 3D point clouds. On the one hand, a binocular lidar system is designed to efficiently capture 3D point clouds of solder joints. On the other hand, a fine-grained method named region attention network (RAN) is designed to detect defects, which attends on the region of interest directly by backpropagation without bounding box annotation. To evaluate the performance of our proposed network, we conduct extensive experiments on a unique dataset built by ourselves. The experimental results show that the region of interest extracted by RAN is consistent with the basis for human evaluation of solder joint quality. Besides, the defect detection results of DoubRAN meet factory requirements.     

Carving for topology simplification of polygonal meshes

The topological complexity of polygonal meshes has a large impact on the performance of various geometric processing algorithms, such as rendering and collision detection algorithms. Several approaches for simplifying topology have been discussed in the literature. These methods operate locally on models, which makes their effect on the topology hard to predict and analyze. Most existing methods tend to exhibit several disturbing artifacts, such as shrinking of the input and splitting of its components. We propose a novel top-down approach for topology simplification that avoids most problems that are common in existing methods. We start with a simple, genus-zero mesh that bounds the input and gradually introduce topologic features by a series of carving operations. This process yields a multiresolution stream of meshes with increasing topologic level of detail. We further present a carving algorithm that is based on constrained Delaunay tetrahedralization. The algorithm first constructs the tetrahedral mesh of the complement of the input with respect to its convex hull. It then proceeds to eliminate tetrahedra in a prioritized manner. We present quality results for two families of meshes that are difficult to simplify by all existing methods known to us: topologically complex meshes and highly clustered meshes.     

面向Java 3D的CAD模型简化与转换

三维图形仿真的一个重要工作就是建立三维模型,若使用已有的模型可以节省一定的时间.文中主要讨论了如何将复杂的CAD模型应用于Java3D建立的图形仿真系统中.首先,对已有的Pro/E模型进行简化,以VRML格式导出,有两种方式应用于Java3D中.一种是直接编辑Pro/E模型导出的VRML格式文件,组织模型之间的链接关系;另一种是将Pro/E模型导出的VRML格式文件导入到3DMax中组织链接关系,然后导出为单一文件.最后联合使用这两种方式将一个复杂的空间机器人模型导入到Java3D,提高了图形数据的重用性和开发的快速性,经实践验证效果理想.     

Perception-based model simplification for motion blur rendering

Motion blur effects are important to motion perception in visual arts, interactive games and animation applications. Usually, such motion blur rendering is quite time consuming, thus blocking the online/interactive use of the effects. Motivated by the human perception in relation to moving objects, this paper presents simplified geometric models that enable to speedup motion blur rendering, which has not been tracked in motion blur rendering specifically. We develop a novel algorithm to simplify models with motion-aware, to preserve the features whose characteristics are perceivable in motion. We deduce the formula to outline the level of detail simplification by the object moving velocity. Using our simplified models, methods for motion blur rendering can achieve the rendering quality as using the original models, and obtain the processing acceleration mostly. The experimental results have shown the effectiveness of our approach, more acceleration with the larger models or faster motion (e.g. for the dragon model with over a million facets, the motion-blur rendering via hierarchical stochastic rasterization is sped up by over 27 times).     

From rendering to tracking point-based 3D models

This paper adds to the abundant visual tracking literature with two main contributions. First, we illustrate the interest of using Graphic Processing Units (GPU) to support efficient implementations of computer vision algorithms, and secondly, we introduce the use of point-based 3D models as a shape prior for real-time 3D tracking with a monocular camera.     

一种粗精度三维模型的生成与快速显示方法

为了解决用传统方法简化后的复杂三维模型表面存在的裂缝,纹理拉伸、缺失、错位等严重影响模型视觉效果的问题,本文提出一种粗精度模型的生成与快速显示方法,该方法利用Billboard作为模型的载体,利用GPU技术实现Billboard随视点的旋转功能,利用视点和Billboard之间的角度动态地更换相应的纹理图片。实验表明,从远处看,用该方法生成的Billboard与原始模型相差无几,完全可以用Billboard替代原始模型,从近处看,用Billboard替代的模型有细微的几何畸变。因此,该方法适合LOD较粗级别的显示,特别是对于拥有大数据量的数字城市和数字地球系统,其不仅保证了远处模型的视觉效果,而且还大大提高了系统的运行效率。     

多特征融合的网格模型简化方法

针对三维网格模型简化过程中的过简化和失真问题,提出一种利用多特征融合的度量方法引导三维网格模型的简化过程。该方法通过分析模型简化的误差度量准则和模型的特征信息,首先利用法向信息加权的二次误差方法度量模型的几何特征信息;然后采用三角形边长比信息加权的挠率度量模型的视觉特征信息;最后融合几何特征信息和视觉特征信息作为模型简化的多特征信息引导模型简化。实验结果表明,该方法可有效保证算法的计算效率,保持简化后模型的形态特征,解决了模型的过简化和失真问题。     

Combined compression and simplification of dynamic 3D meshes

We present a new approach to dynamic mesh compression, which combines compression with simplification to achieve improved compression results, a natural support for incremental transmission and level of detail. The algorithm allows fast progressive transmission of dynamic 3D content. Our scheme exploits both temporal and spatial coherency of the input data, and is especially efficient for the case of highly detailed dynamic meshes. The algorithm can be seen as an ultimate extension of the clustering and local coordinate frame (LCF)‐based approaches, where each vertex is expressed within its own specific coordinate system. The presented results show that we have achieved better compression efficiency compared to the state of the art methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Shape‐adaptive 3‐D mesh simplification based on local optimality measurement

Mesh simplification is the process of reducing the number of triangles in a mesh representation of object surface. For a given level of detail or error tolerance, the conventional mesh simplification algorithms maximize the edge length globally, without explicitly considering local object shape. In this paper, we present a shape‐adaptive mesh simplification algorithm that locally maximizes edge length, depending on local shape. The proposed algorithm achieves shape‐adaptive simplification by iteratively maximizing edges between vertices, based on comparison with the ‘optimal’ edge lengths derived from local directional curvatures for a given error tolerance. Edge‐based processing facilitates the local shape adaptation and preserves sharp features. Experimental results demonstrate the efficacy of the proposed algorithm, by showing good visual quality and extremely small approximation error. Copyright © 2003 John Wiley & Sons, Ltd.     

User-assisted simplification method for triangle meshes preserving boundaries

This paper proposes a user-assisted mesh simplification method applied to CAD models converted to triangle meshes. This work offers the possibility of simplifying each subobject independently and at different levels of detail. This way, the user can simplify the whole model and then modify some parts, by simplifying more or by refining the desired subobjects. This can be performed while the total number of triangles in the simplified model is maintained. In this method any error metric based on an edge collapse operation can be used. Boundaries between subobjects are preserved and important attributes in the final aspect of simplified models, like normals and texture coordinates, are also considered.     

An efficient and low-error mesh simplification method based on torsion detection

To preserve the major characteristics of the simplified model, this study proposes the use of torsion detection to improve the quadric error metric of vertex-pair contraction, and retain the physical features of the models. Besides keeping the physical features of the models, the proposed method also decreases the preprocessing time cost associated with analysis. To verify the conclusion, this research not only presents the effects of simplification and compares them with the vertex-pair contraction, but also employs Metro detection and image comparison to verify the error measurements. The experimental results demonstrate that the proposed method improves the error rate and keeps the precision of the object features efficiently.     

Point cloud simplification for the boundary preservation based on extracted four features

This paper proposes a simplification algorithm based on four feature parameters, aiming at solving the problem that the edge features cannot be retained due to the incompletely extracted sharp features during point cloud simplification. Firstly, K neighborhood searching is carried out for point cloud, and K neighborhood points are quickly found by a dynamic grid method. Then, four features including: the curvature of the point, the average of the normal angle of a point from a neighborhood point, the average distance between the point and the neighborhood point and the distance between the point and the center of gravity of the neighborhood point, are calculated according to the K neighborhood of the data point. The four parameters are used to define the feature discrimination parameters and feature thresholds, to compare the size and extract the feature points; finally, the non-feature points are reduced twice by the method of the bounding box, and the reduced point cloud and feature points are spliced to achieve the purpose of simplification. The experimental results show that the distance between the point and the center of gravity of the neighborhood has a great influence on the simplified model boundary, which effectively guarantees the accuracy of the simplified model.     

An effective quad-dominant meshing method for unorganized point clouds

To perform quad meshing on raw point clouds, existing algorithms usually require a time-consuming parameterization or Voronoi space partition process. In this paper, we propose an effective method to generate quad-dominant meshes directly from unorganized point clouds. In the proposed method, we first apply Marinov’s curvature tensor optimization to the input point cloud to reduce the umbilical regions in order to obtain a smooth curvature tensor. We then propose an efficient marching scheme to extract the curvature lines with controllable density from the point cloud. Finally, we apply a specialized K-Dimension (KD) tree structure, which converts the nearest neighbor searching problem into a sorting problem, to efficiently estimate the intersections of curvature lines and recover the topology of the quad-dominant meshes. We have tested the proposed method on different point clouds. Our results show that the proposed method produces good quality meshes with high computational efficiency and low memory requirement.     

保持几何模式的城市道路格网简化方法

提出一种保持格网几何模式的城市路网简化方法。该方法首先在格网自动识别的基础上对格网间的空间关系进行计算,将存在包含关系的格网合并成更高等级的格网;然后对格网进行连通划分,以格网指数、主方向、延展指数作为格网信息分量进行搜索,删除使格网信息量损失最小的路段,实现格网的简化;最后,以武汉与苏黎世城市路网数据作为实验数据进行格网简化。实验结果表明,本文方法在简化格网的同时能够较好地保持格网的模式特征,为小屏幕地图可视化、空间数据多尺度表达和渐进传输等提供较好的数据源。     

R-VPCG: RGB image feature fusion-based virtual point cloud generation for 3D car detection

Although 3D object detection methods based on feature fusion have made great progress, the methods still have the problem of low precision due to sparse point clouds. In this paper, we propose a new feature fusion-based method, which can generate virtual point cloud and improve the precision of car detection. Considering that RGB images have rich semantic information, this method firstly segments the cars from the image, and then projected the raw point clouds onto the segmented car image to segment point clouds of the cars. Furthermore, the segmented point clouds are input to the virtual point cloud generation module. The module regresses the direction of car, then combines the foreground points to generate virtual point clouds and superimposed with the raw point cloud. Eventually, the processed point cloud is converted to voxel representation, which is then fed into 3D sparse convolutional network to extract features, and finally a region proposal network is used to detect cars in a bird’s-eye view. Experimental results on KITTI dataset show that our method is effective, and the precision have significant advantages compared to other similar feature fusion-based methods.     

面向点云的三维物体识别方法综述

随着三维扫描技术的快速发展,获取各类场景的点云数据已经非常简单快捷;加之点云数据具备不受光照、阴影、纹理的影响等优势,基于点云的三维物体识别已成为计算机视觉领域的研究热点。首先,对近年来面向点云数据的三维物体识别方法进行归纳和总结;然后,对已有方法的优势及缺点进行分析;最后,指出点云物体识别中所面临的挑战及进一步的研究方向。     

逆向工程中的三维光学检测点云采样技术研究

针对大型复杂曲面产品三维光学检测技术在逆向工程中存在获取点云数据密度大、冗余点繁多、处理任务繁重等问题, 对光学检测获得的海量点云预处理技术和工作流程进行了研究, 提出了点云采样时的最终判据Δ(S, S′)。该判据在满足高精度、快速度、适简度的原则下以达到给定的最大允许误差或者指定点的数目为目标, 适用于重建几何精度要求较高的场合。以某大型水轮机叶片为例进行了采样和逆向建模精度分析, 证明了三维光学检测点云采样技术的正确性和可行性。     

网络环境下道路三维整体建模与简化方法

为实现网络环境下道路工程的三维可视化,对其中的关键技术：顾及约束的整体模型构建及模型简化方法进行了研究。基于约束Delaunay三角网构建理论,建立了外形与内部拓扑关系均为整体的道路三维模型。提出了顾及道路约束边界的半边折叠误差度量方法,采用半边折叠操作,在服务器端对道路模型进行整体简化,并建立操作层次树存储操作记录;提出了约束边优先细化,延迟简化的视相关策略,结合视相关重构准则,减少网络可视化所需传输的数据量,在客户端实现了道路三维模型的快速重构。基于上述原理方法开发了相关系统,已在高速公路的网络建设管理中成功应用。