Model-Based Object Recognition Using Geometric Invariants of Points and Lines

In this paper, we derive new geometric invariants for structured 3D points and lines from single image under projective transform, and we propose a novel model-based 3D object recognition algorithm using them. Based on the matrix representation of the transformation between space features (points and lines) and the corresponding projected image features, new geometric invariants are derived via the determinant ratio technique. First, an invariant for six points on two adjacent planes is derived, which is shown to be equivalent to Zhu's result [1], but in simpler formulation. Then, two new geometric invariants for structured lines are investigated: one for five lines on two adjacent planes and the other for six lines on four planes. By using the derived invariants, a novel 3D object recognition algorithm is developed, in which a hashing technique with thresholds and multiple invariants for a model are employed to overcome the over-invariant and false alarm problems. Simulation results on real images show that the derived invariants remain stable even in a noisy environment, and the proposed 3D object recognition algorithm is quite robust and accurate.     

基于对象图例及其拓扑关系识别的二维工程CAD图纸矢量化方法

为实现二维工程CAD图纸矢量化,提出一种基于对象图例及其拓扑关系识别的矢量化方法。该方法首先针对对象图例的几何属性,提出基于HOG(Histogram of Gradient)和SVM(Support Vector Machine)的多类对象图例分类方法,然后通过提取对象的环形分割特征识别子类对象图例,接着利用基于连通域标记方法实现对象图例拓扑关系的识别。结果表明,本文算法能够有效识别图纸中的对象及其拓扑关系,对于图纸中常见的图例线条断裂、模糊等问题具有鲁棒性。基于本文算法提取的对象几何和拓扑信息可以为后续的图纸矢量化奠定基础,相关关键技术的探索性研究对后续工程图纸矢量化研究具有一定启发性。     

基于三维网格物体的鲁棒性盲水印方案

提出了一种基于3D网格物体的鲁棒性盲水印方案。先将3D物体模型转换到仿射不变空间,然后把顶点坐标转换到球面坐标,利用球面坐标来嵌入水印。由于在仿射不变空间下,3D物体模型不受平移、旋转和比例变换的影响,故该水印方案对于此类变化具有很强的鲁棒性。试验结果也证明了这一点,且该水印具有良好的不可见性。     

基于小波变换的三维网格物体盲水印方案

提出了一种基于小波变换的3D网格物体鲁棒性盲水印方案.首先将3D物体模型转换到仿射不变空间,抽取三维物体重心到顶点的距离生成一个一维的离散信号;将该离散信号进行小波变换,改变其高频系数以嵌入水印,然后经过小波逆变换生成带水印的3D物体模型.在仿射不变空间下,实现了3D物体模型对平移、旋转、比例变换的鲁棒性,采用小波变换使3D模型具有很强的水印不可见性以及一定的噪声鲁棒性.试验结果表明,该方法不仅对于旋转、平移、比例变换具有很强的鲁棒性,而且具有良好的水印不可见性.     

Stereo‐Consistent Contours in Object Space

Notebook scribbles, art or technical illustrations—line drawings are a simplistic method to visually communicate information. Automated line drawings often originate from virtual 3D models, but one cannot trivially experience their three‐dimensionality. This paper introduces a novel concept to produce stereo‐consistent line drawings of virtual 3D objects. Some contour lines do not only depend on an objects geometry, but also on the position of the observer. To accomplish consistency between multiple view positions, our approach exploits geometrical characteristics of 3D surfaces in object space. Established techniques for stereo‐consistent line drawings operate on rendered pixel images. In contrast, our pipeline operates in object space using vector geometry, which yields many advantages: The position of the final viewpoint(s) is flexible within a certain window even after the contour generation, e.g. a stereoscopic image pair is only one possible application. Such windows can be concatenated to simulate contours observed from an arbitrary camera path. Various types of popular contour generators can be handled equivalently, occlusions are natively supported and stylization based on geometry characteristics is also easily possible.     

可减少模型简化误差的边折叠简化算法及应用

基于三角形网格边折叠简化思想，提出一种基于边顶点重要度简化算法，简化算法能有效保持模型局部特征，减小简化模型和原始模型之间的误差；采用一种改进的三角形网格数据结构，利用二叉树对顶点重要度进行快速排序并记录三角形合并关系，得到所需分辨率下的近似网格模型。数据结构具有层次清楚、操作简单、可扩充性等特点，能有效支持多分辨率简化与快速可视化。     

Plane-based optimization for 3D object reconstruction from single line drawings

In previous optimization-based methods of 3D planar-faced object reconstruction from single 2D line drawings, the missing depths of the vertices of a line drawing (and other parameters in some methods) are used as the variables of the objective functions. A 3D object with planar faces is derived by finding values for these variables that minimize the objective functions. These methods work well for simple objects with a small number N of variables. As N grows, however, it is very difficult for them to find expected objects. This is because with the nonlinear objective functions in a space of large dimension N, the search for optimal solutions can easily get trapped into local minima. In this paper, we use the parameters of the planes that pass through the planar faces of an object as the variables of the objective function. This leads to a set of linear constraints on the planes of the object, resulting in a much lower dimensional nullspace where optimization is easier to achieve. We prove that the dimension of this nullspace is exactly equal to the minimum number of vertex depths which define the 3D object. Since a practical line drawing is usually not an exact projection of a 3D object, we expand the nullspace to a larger space based on the singular value decomposition of the projection matrix of the line drawing. In this space, robust 3D reconstruction can be achieved. Compared with two most related methods, our method not only can reconstruct more complex 3D objects from 2D line drawings, but also is computationally more efficient.     

Visual saliency guided textured model simplification

Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods.     

一种DCT变换的三维网格物体盲水印方法

提出了一种基于DCT变换的3D网格物体鲁棒性盲水印方案。首先将3D物体模型转换到仿射不变空间,抽取三维物体重心到顶点的距离生成一个一维的离散信号,将该离散信号进行DCT变换,改变其系数以嵌入水印。然后经过逆DCT变换生成带水印的3D物体模型。在仿射不变空间下,实现了3D物体模型对平移、旋转、比例变换的鲁棒性,采用DCT变换使3D模型具有很强的水印不可见性,而且具有一定的噪声鲁棒性。试验结果也表明该方法不仅对于旋转、平移、比例变换具有很强的鲁棒性,而且具有良好的水印不可见性。     

基于混合频谱信号编码的网格纹理平滑

网格纹理平滑技术要求既能保持模型大尺度结构特征又能去除模型小尺度纹理.然而当模型小尺度纹理与噪声相差较大时,大多数网格光顺算法会将网格纹理识别为特征加以保持,而无法有效将其去除;现有的基于谱分析的网格光顺方法尽管能有效去除网格纹理,但又无法同时保持模型大尺度结构特征.为解决该问题,本文提出一种基于混合频谱信号编码的低通...     

Recovering solid geometric object from single line drawing image

Many educational materials contain a lot of solid geometric figures. The solid geometric objects in these figures are usually drawn as 2D line drawings thus have lost their 3D information. This paper presents a method to recover the 3D information of the solid geometric object from single line drawing image taken from the geometric books, which would be used to help the users better present and understand the solid geometric object on their mobile devices. The main advantage of our method is the abilitYTo handle inaccurately processed sketches as opposed to the previous methods which require perfect line drawings as inputs. Our method consists of three main steps as follows. First, the sketch of the input line drawing image is automatically extracted and further represented as an undirected graph. Second, candidate 3D models from the pre-built 3D model database are found by graph similarity-based searching and sub-graph isomorphism matching. Third, for each candidate 3D model, the model parameters, the rotation and the translation aligning the model with the sketch are found by minimizing an objective function which is composed of the residuals between the vertices of the sketch and the 2D projections of the candidate model’s vertices, and an optimal reconstruction solution is further selected as the final result. Extensive experimental results demonstrate the effectiveness and robustness of our method for recovering the solid geometric object from single line drawing image.     

A complete system for the intelligent interpretation of engineering drawings

Converting paper-based engineering drawings into CAD model files is a tedious process. Therefore, automating the conversion of such drawings represents tremendous time and labor savings. We present a complete system which interprets such 2D paper-based engineering drawings, and outputs 3D models that can be displayed as wireframes. The system performs the detection of dimension sets, the extraction of object lines, and the assembly of 3D objects from the extracted object lines. A knowledge-based method is used to remove dimension sets and text from ANSI engineering drawings, a graphics recognition procedure is used to extract complete object lines, and an evidential rule-based method is utilized to identify view relationships. While these methods are the subject of several of our previous papers, this paper focuses on the 3D interpretation of the object. This is accomplished using a technique based on evidential reasoning and a wide range of rules and heuristics. The system is limited to the interpretation of objects composed of planar, spherical, and cylindrical surfaces. Experimental results are presented. Received December 2, 1998 / Revised June 18, 1999     

A 2D/3D model-based object tracking framework

This paper presents a robust framework for tracking complex objects in video sequences. Multiple hypothesis tracking (MHT) algorithm reported in (IEEE Trans. Pattern Anal. Mach. Intell. 18(2) (1996)) is modified to accommodate a high level representations (2D edge map, 3D models) of objects for tracking. The framework exploits the advantages of MHT algorithm which is capable of resolving data association/uncertainty and integrates it with object matching techniques to provide a robust behavior while tracking complex objects. To track objects in 2D, a 4D feature is used to represent edge/line segments and are tracked using MHT. In many practical applications 3D models provide more information about the object's pose (i.e., rotation information in the transformation space) which cannot be recovered using 2D edge information. Hence, a 3D model-based object tracking algorithm is also presented. A probabilistic Hausdorff image matching algorithm is incorporated into the framework in order to determine the geometric transformation that best maps the model features onto their corresponding ones in the image plane. 3D model of the object is used to constrain the tracker to operate in a consistent manner. Experimental results on real and synthetic image sequences are presented to demonstrate the efficacy of the proposed framework.     

Fast Keypoint Recognition Using Random Ferns

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as the number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image data sets containing very significant perspective changes.     

一种保持纹理的网面简化算法

在计算机视觉领域,三维网面的简化不仅要求保持物体形状和拓扑关系,还要求保持物体表面法线,纹理,颜色和边缘等物体特征,以使计算机视觉系统能有效地表示,描述,识别和理解物体和场景,为此讨论了一种基于边操作（边收缩,边分裂）,并具有颜色或灰度纹理特征保持的三维网面的简化算法,该算法将网面不对称最大距离作为形状改变测度,将邻域内颜色或灰度最大改变量作为纹理改变测试,从而在大量简化模型数据的同时,有效地保持了模型的几何形状,拓扑关系,颜色或灰度特征,以及网面顶点均匀分布。     

Landmark transfer with minimal graph

We present an efficient and robust algorithm for the landmark transfer on 3D meshes that are approximately isometric. Given one or more custom landmarks placed by the user on a source mesh, our method efficiently computes corresponding landmarks on a family of target meshes. The technique is useful when a user is interested in characterization and reuse of application-specific landmarks on meshes of similar shape (for example, meshes coming from the same class of objects). Consequently, across a set of multiple meshes consistency is assured among landmarks, regardless of landmark geometric distinctiveness. The main advantage of our method over existing approaches is its low computation time. Differently from existing non-rigid registration techniques, our method detects and uses a minimum number of geometric features that are necessary to accurately locate the user-defined landmarks and avoids performing unnecessary full registration. In addition, unlike previous techniques that assume strict consistency with respect to geodesic distances, we adopt histograms of geodesic distance to define feature point coordinates, in order to handle the deviation of isometric deformation. This allows us to accurately locate the landmarks with only a small number of feature points in proximity, from which we build what we call a minimal graph. We demonstrate and evaluate the quality of transfer by our algorithm on a number of Tosca data sets.     

3D object model reconstruction from image sequence based on photometric consistency in volume space

This paper presents a system that can reconstruct a photorealistic 3D object model from an image sequence captured at arbitrary viewpoints. The whole system consists of four steps: camera calibration, volumetric modeling, polygonal model formation and texture mapping. We adopt the shape-from-silhouette approach for volumetric modeling. There are two common types of object surface that are difficult to reconstruct—textureless surface and concave surface. To tackle the problems, we propose to perform the volumetric modeling based on the constraints of viewpoint proximity and photometric consistency in the volume space. The volumetric model is converted to the mesh model for efficient manipulation. Finally, the texture map is generated from the image sequence to give the 3D model a photorealistic appearance. Some reconstructed object models are presented to demonstrate the superior performance of our system as compared with the conventional modeling technique based on the photo-consistency in the image space.     

Efficiently computing exact geodesic loops within finite steps

Closed geodesics, or geodesic loops, are crucial to the study of differential topology and differential geometry. Although the existence and properties of closed geodesics on smooth surfaces have been widely studied in mathematics community, relatively little progress has been made on how to compute them on polygonal surfaces. Most existing algorithms simply consider the mesh as a graph and so the resultant loops are restricted only on mesh edges, which are far from the actual geodesics. This paper is the first to prove the existence and uniqueness of geodesic loop restricted on a closed face sequence; it contributes also with an efficient algorithm to iteratively evolve an initial closed path on a given mesh into an exact geodesic loop within finite steps. Our proposed algorithm takes only an O(k) space complexity and an O(mk) time complexity (experimentally), where m is the number of vertices in the region bounded by the initial loop and the resultant geodesic loop, and k is the average number of edges in the edge sequences that the evolving loop passes through. In contrast to the existing geodesic curvature flow methods which compute an approximate geodesic loop within a predefined threshold, our method is exact and can apply directly to triangular meshes without needing to solve any differential equation with a numerical solver; it can run at interactive speed, e.g., in the order of milliseconds, for a mesh with around 50K vertices, and hence, significantly outperforms existing algorithms. Actually, our algorithm could run at interactive speed even for larger meshes. Besides the complexity of the input mesh, the geometric shape could also affect the number of evolving steps, i.e., the performance. We motivate our algorithm with an interactive shape segmentation example shown later in the paper.