High Efficient Methods of Content-based 3D Model Retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the ＂Shape Distribution （D2）＂ algorithm, and a new algorithm named ＂Quick D2＂ is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, ＂3D Searcher＂, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.

Ontology Framework for Content-Based 3D Model Retrieval

The goal of the research on ontology framework for content-based 3D model retrieval is to develop a rich set of 3D model semantic representation so that both humans and machines can generate and understand model descriptions and processing for fast efficient retrieval from model collections. The purpose of ontology development for content-based 3D model retrieval is intended to describe model information regardless of storage, feature extraction and creation. The ontology includes the information on media features, low level visual descriptors, non media features of 3D model and their relationships. It is implemented in protege 3.1.

Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

3D Engineering Model Retrieval Based on Enhanced Shape Distributions

To reuse and share the valuable knowledge embedded in repositories of engineering models for accelerating the design process, improving product quality, and reducing costs, it is crucial to devise search engines capable of matching 3D models efficiently and effectively. In this paper, an enhanced shape distributions-based technique of using geometrical and topological information to search 3D engineering models represented by polygonal meshes was presented. A simplification method of polygonal meshes was used to simplify engineering model as the pretreatment for generation of sample points. The method of sampling points was improved and a pair of functions that was more sensitive to shape was employed to construct a 2D shape distribution. Experiments were conducted to evaluate the proposed algorithm utilizing the Engineering Shape Benchmark (ESB) database. The experiential results suggest that the search effectiveness is significantly improved by enforcing the simplification and enhanced shape distributions to engineering model retrieval.

Multi-level spherical moments based 3D model retrieval

In this paper a novel 3D model retrieval method that employs multi-level spherical moment analysis and relies on voxelization and spherical mapping of the 3D models is proposed. For a given polygon-soup 3D model, first a pose normalization step is done to align the model into a canonical coordinate frame so as to define the shape representation with respect to this orientation. Afterward we rasterize its exterior surface into cubical voxel grids, then a series of homocentric spheres with their center superposing the center of the voxel grids cut the voxel grids into several spherical images. Finally moments belonging to each sphere are computed and the moments of all spheres constitute the descriptor of the model. Experiments showed that Euclidean distance based on this kind of feature vector can distinguish different 3D models well and that the 3D model retrieval system based on this arithmetic yields satisfactory performance.

Distributed Cluster Based 3D Model Retrieval with Map-Reduce

View-based 3D model retrieval methods are attracted intensive research attentions due to the high expression and stable features. In the paper, the bag-of-words (BOW) standardization based SIFT feature were extracted from three projection views of a 3D model, and then the distributed K-means cluster algorithm based on a Hadoop platform was employed to compute feature vectors and cluster 3D models. In order to get precise initial cluster center, the maximum and minimum principle based Canopy algorithm was also presented. The similarity of models was determined by the distance between the query model and each cluster center, and the cluster which nearest to the query model will be return as retrieval results. The simulations indicated that the proposed method had good results in terms of 3D model retrieval accuracy and retrieval time efficiency.

Dynamic Hyperlinker: Innovative Solution for 3D Video Content Search and Retrieval

Recently, 3D display technology, and content creation tools have been undergone rigorous development and as a result they have been widely adopted by home and professional users. 3D digital repositories are increasing and becoming available ubiquitously. However, searching and visualizing 3D content remains a great challenge. In this paper, we propose and present the development of a novel approach for creating hypervideos, which ease the 3D content search and retrieval. It is called the dynamic hyperlinker for 3D content search and retrieval process. It advances 3D multimedia navigability and searchability by creating dynamic links for selectable and clickable objects in the video scene whilst the user consumes the 3D video clip. The proposed system involves 3D video processing, such as detecting/tracking clickable objects, annotating objects, and metadata engineering including 3D content descriptive protocol. Such system attracts the attention from both home and professional users and more specifically broadcasters and digital content providers. The experiment is conducted on full parallax holoscopic 3D videos “also known as integral images”.

Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry:Application to the 2016 Kumamoto Earthquake

Conventional synthetic aperture radar(SAR)interferometry(InSAR)has been successfully used to precisely measure surface deformation in the line-of-sight(LOS)direction,while multiple-aperture SAR interferometry(MAI)has provided precise surface deformation in the along-track(AT)direction.Integration of the InSAR and MAI methods enables precise measurement of the two-dimensional(2D)deformation from an interferometric pair;recently,the integration of ascending and descending pairs has allowed the observation of precise three-dimensional(3D)deformation.Precise 3D deformation measurement has been applied to better understand geological events such as earthquakes and volcanic eruptions.The surface deformation related to the 2016 Kumamoto earthquake was large and complex near the fault line;hence,precise 3D deformation retrieval had not yet been attempted.The objectives of this study were to①perform a feasibility test of precise 3D deformation retrieval in large and complex deformation areas through the integration of offset-based unwrapped and improved multiple-aperture SAR interferograms and②observe the 3D deformation field related to the 2016 Kumamoto earthquake,even near the fault lines.Two ascending pairs and one descending the Advanced Land Observing Satellite-2(ALOS-2)Phased Array-type L-band Synthetic Aperture Radar-2(PALSAR-2)pair were used for the 3D deformation retrieval.Eleven in situ Global Positioning System(GPS)measurements were used to validate the 3D deformation measurement accuracy.The achieved accuracy was approximately 2.96,3.75,and 2.86 cm in the east,north,and up directions,respectively.The results show the feasibility of precise 3D deformation measured through the integration of the improved methods,even in a case of large and complex deformation.

Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion

In this paper, we propose a dynamic multi-descriptor fusion （DMDF） approach to improving the retrieval accuracy of 3-dimensional （3D） model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection （ARMS） approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement （AQPM） approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy.

三维CAD模型检索技术研究现状与发展分析

对目前三维CAD模型检索技术的研究现状和发展趋势进行了阐述。首先给出三维模型检索的体系结构,并从文本检索、内容检索和语义检索3方面对三维CAD模型检索技术国内外研究现状进行了全面论述;分析总结了现有三维CAD模型检索系统及三维CAD模型检索技术的应用;最后对三维CAD模型检索技术存在的问题及未来的发展方向进行了展望。三维CAD模型检索技术将为概念设计、详细设计、工艺/工装设计等产品各设计阶段提供全方位的检索支持手段,可有效促进企业产品模型相关设计和制造信息的重用。

基于距离-夹角形状分布的三维CAD模型检索算法

为了更好地实现三维CAD模型的有效重用,提出一种三维CAD模型距离-夹角形状分布检索算法。首先在CAD模型表面随机取点,计算任意两点构成的有向线段的距离和该有向线段与端点法向的夹角;然后以距离和夹角为坐标轴构建距离-夹角平面网格,统计网格中随机点出现的频次,从而构建模型的距离-夹角分布矩阵;最后采用Manhattan距离度量法计算矩阵的相似值,用以评价模型间的相似程度。实验结果表明,该算法可以较好地实现三维CAD模型检索,检索性能优于已有的形状分布检索算法,能够满足实际应用需求。

超强台风韦帕(0713)螺旋雨带中尺度结构双多普勒雷达研究

超强台风"韦帕"(Wipha)是2007年登陆中国大陆最强的台风,在浙江省造成了特大暴雨。利用宁波和舟山双多普勒天气雷达同步观测资料,对"韦帕"的两条螺旋雨带进行了双雷达三维风场反演;并综合利用组网雷达拼图数据等资料,对螺旋雨带的三维精细结构进行了分析。研究表明:(1)两条螺旋雨带的三维结构有很多相似之处。螺旋雨带内部低层有多个强回波区,水平速度大值区主要分布在强回波带上;强回波带的低层有较强的上升气流,最强上升气流超过4 m/s。在螺旋雨带中存在多个辐合辐散对、上升下沉气流对,这对于螺旋雨带的维持和进一步发展具有重要作用。在沿着台风中心的垂直剖面内,螺旋雨带内部的强回波区向雨带外侧倾斜。雨带外侧2 km高度以下的低层有较强的内流,最大值为5 m/s;雨带内侧有较强的外流,2 km高度以上均受外流控制;内流和外流在雨带中部低层汇合抬升。切向速度的强中心出现在3 km高度,速度值随高度增加而逐渐减小。(2)两个时段的螺旋雨带也存在差异。前一个时段的螺旋雨带对流发展更旺盛,45 dBZ的回波高度为4.8 km,而后一个时段的螺旋雨带45 dBZ的回波高度仅3.2 km。垂直剖面内,前一个时刻螺旋雨带低层辐合更强,最强辐合值超过-15×10-4s-1,正是由于低层的强辐合和充足的水汽供应,才使得雨带内部中低层的回波发展旺盛。

基于异构特征LDA的三维模型分类及检索

三维模型检索领域中基于内容的检索方法不能充分表达模型语义信息。针对该问题,提出一种包含语义分类信息的三维模型检索方法。采用人工分类信息、有限的语义标准信息等构建异构语义信息网络,并将其转换为三维模型的异构语义特征,在此基础上使用包含模型语义特征的主题分类方法,并将其应用于模型检索中。实验结果表明,与基于内容的三维模型检索方法相比,该方法能提高三维模型检索的准确性。

A Survey on Partial Retrieval of 3D Shapes

Content-based shape retrieval techniques can facilitate 3D model resource reuse, 3D model modeling, object recognition, and 3D content classification. Recently more and more researchers have attempted to solve the problems of partial retrieval in the domain of computer graphics, vision, CAD, and multimedia. Unfortunately, in the literature, there is little comprehensive discussion on the state-of-the-art methods of partial shape retrieval. In this article we focus on reviewing the partial shape retrieval methods over the last decade, and help novices to grasp latest developments in this field. We first give the definition of partial retrieval and discuss its desirable capabilities. Secondly, we classify the existing methods on partial shape retrieval into three classes by several criteria, describe the main ideas and techniques for each class, and detailedly compare their advantages and limits. We also present several relevant 3D datasets and corresponding evaluation metrics, which are necessary for evaluating partial retrieval performance. Finally, we discuss possible research directions to address partial shape retrieval.

3D object retrieval based on histogram of local orientation using one-shot score support vector machine

In this paper, a content based descriptor is pro- posed to retrieve 3D models, which employs histogram of local orientation （HLO） as a geometric property of the shape. The proposed 3D model descriptor scheme consists of three steps. In the first step, Poisson equation is utilized to define a 3D model signature. Next, the local orientation is calculated for each voxel of the model using Hessian matrix. As the final step, a histogram-based 3D model descriptor is extracted by accumulating the values of the local orientation in bins. Due to efficiency of Poisson equation in describing the models with various structures, the proposed descriptor is capable of discriminating these models accurately. Since, the inner vox- els have a dominant contribution in the formation of the de- scriptor, sufficient robustness against noise can be achieved. This is because the noise mostly influences the boundary vox- els. Furthermore, we improve the retrieval performance us- ing support vector machine based one-shot score （SVM-OSS） similarity measure, which is more efficient than the conven- tional methods to compute the distance of feature vectors. The rotation normalization is performed employing the prin- cipal component analysis. To demonstrate the applicability of HLO, we implement experimental evaluations of precision- recall curve on ESB, PSB and WM-SHREC databases of 3D models. Experimental results validate the effectiveness of the proposed descriptor compared to some current methods.

Local features and manifold ranking coupled method for sketch-based 3D model retrieval

3D model retrieval virtual reality applications. In can benefit many downstream this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors. Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians （DOG） method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.

Web3D Learning Framework for 3D Shape Retrieval Based on Hybrid Convolutional Neural Networks

With the rapid development of Web3 D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3 D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks(CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3 D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3 D furniture,and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches.

Efficient View-Based 3-D Object Retrieval via Hypergraph Learning

View-based 3-D object retrieval has become an emerging topic in recent years,especially with the fast development of visual content acquisition devices,such as mobile phones with cameras.Extensive research efforts have been dedicated to this task,while it is still difficult to measure the relevance between two objects with multiple views.In recent years,learning-based methods have been investigated in view-based 3-D object retrieval,such as graph-based learning.It is noted that the graph-based methods suffer from the high computational cost from the graph construction and the corresponding learning process.In this paper,we introduce a general framework to accelerate the learning-based view-based 3-D object matching in large scale data.Given a query object Q and one object O from a 3-D dataset D,the first step is to extract a small set of candidate relevant 3-D objects for object O.Then multiple hypergraphs can be constructed based on this small set of 3-D objects and the learning on the fused hypergraph is conducted to generate the relevance between Q and O,which can be further used in the retrieval procedure.Experiments demonstrate the effectiveness of the proposed framework.

Review of techniques for motion capture data processing

In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.