Content-based 3D Model Retrieval Research Articles

Multi-feature 3D model retrieval using view-based techniques

ABSTRACT Most research on 3D model retrieval focus on shape as the main feature used for search and retrieval. In this paper, we explore augmenting 3D shape-based similarity measures by combining shape with color features. There are already numerous techniques for content-based 3D model retrieval utilizing shape, some use global shape features, shape distributions, or view-based methods. This paper uses a view-based approach using 2D projections of 3D models taken from different viewpoints. The main problem with view-based techniques is how to normalize the rotation of the mesh in 3D space to properly align the model to the projection planes. In our work, we use Normal-vectors PCA (NPCA). The 3D model is first projected onto three planes using their normal vectors and gray-level images (inner elevations) that describe the depth information are generated. The angular radial transform (ART) from MPEG-7 is then used on these inner elevations to extract a feature descriptor. These descriptors are called the inner elevation descriptor (IED) and are used to match the query with the database object. This shape feature is then augmented using color histograms considering perceptual color similarity. It is then tested on the Princeton Shape Benchmark and compared to well-known approaches.

A novel 3D model retrieval system based on three-view sketches

© 2016 - IOS Press and the authors. All rights reserved.3D models can be used in 3D printing and many other areas. At present, there are a lot of researches on 3D model retrieval and sketch is considered to be important for 3D model retrieval. In this paper, we develop a new 3D model retrieval prototype system based on style-sensitive 3D model retrieval method and three-view user sketches. We also implement user-friendly graphic interfaces for the 3D model retrieval system. We explore the performance of the system by conducting a series of 3D model retrieval experiments on the Princeton shape benchmark data set. Experimental results show that the new retrieval system can obtain satisfactory retrieval results. And some results of the new method are superior to some content-based 3D model retrieval methods, in terms of both quantitative search performance metrics and qualitatively measured user search experiences.

3D model retrieval scheme based on fuzzy clustering for physical descriptors

With the rapid development of 3D scanners, graphic accelerated hardware and modeling tools, the application of 3D model databases is growing in both numbers and size. There is a pressing need for effective content-based 3D model retrieval methods. In this paper, a novel 3D model retrieval scheme called Fuzzy Clustering for Physical Descriptor is proposed. First, a multi-scale indexing of 3D model database is generated for fast matching by using fuzzy clustering for a concise descriptor named as partial physical descriptor, and then a novel and effectiveness descriptor named as the global physical descriptor is embedded into feature matching with the generated indexing for obtaining more accurate retrieval results. Several retrieval performance measures demonstrate that the proposed approach is superior to other methods and robust to the missing polygons and noise.

Annotation and retrieval system of CAD models based on functional semantics

CAD model retrieval based on functional semantics is more significant than content-based 3D model retrieval during the mechanical conceptual design phase. However, relevant research is still not fully discussed. Therefore, a functional semantic-based CAD model annotation and retrieval method is proposed to support mechanical conceptual design and design reuse, inspire designer creativity through existing CAD models, shorten design cycle, and reduce costs. Firstly, the CAD model functional semantic ontology is constructed to formally represent the functional semantics of CAD models and describe the mechanical conceptual design space comprehensively and consistently. Secondly, an approach to represent CAD models as attributed adjacency graphs(AAG) is proposed. In this method, the geometry and topology data are extracted from STEP models. On the basis of AAG, the functional semantics of CAD models are annotated semi-automatically by matching CAD models that contain the partial features of which functional semantics have been annotated manually, thereby constructing CAD Model Repository that supports model retrieval based on functional semantics. Thirdly, a CAD model retrieval algorithm that supports multi-function extended retrieval is proposed to explore more potential creative design knowledge in the semantic level. Finally, a prototype system, called Functional Semantic-based CAD Model Annotation and Retrieval System(FSMARS), is implemented. A case demonstrates that FSMARS can successfully botain multiple potential CAD models that conform to the desired function. The proposed research addresses actual needs and presents a new way to acquire CAD models in the mechanical conceptual design phase.

A Three-Dimensional Mechanical Model Preprocessing Method Based on Modeling Software

Three-dimensional model is high degree of freedom information. In order to realize content-based 3D model retrieval, normalization preprocessing of 3D models is needed. This paper have proposed a normalization method with 3D modeling software based on center of mass, principal axis of inertia and volume parameters to carry out coordinate system normalization of three-dimensional mechanical entity models. In this paper, Specific method is listed. Application examples are given to show the feasibility of this method.

3D model retrieval and classification by semi-supervised learning with content-based similarity

The rapid development of 3D digital technology has led to an increasing volume of 3D model data. In addressing the management of such large scale data, effective content-based 3D model retrieval and recognition methods are highly desirable. In 3D model retrieval and recognition tasks, the distance measure between two 3D models plays an important role. In this paper, we propose a novel 3D model retrieval and recognition method that employs both a distance histogram and 3D moment invariants as features that are invariant to 3D object scaling, translation, and rotation. Disjoint information is used to measure the distance between the feature histograms, and the Euclidean distance is applied in calculating the distance between two moment features. These measures are then combined as the 3D model distance. Using this distance measure, the relationships between all 3D models in the dataset are formulated as a graph structure. A semi-supervised learning process is then conducted to estimate the relevance among the 3D models, and this is employed for 3D model retrieval and classification. To evaluate the effectiveness of the proposed method, we conduct experiments on two datasets. Experimental results and a comparison with state-of-the-art methods demonstrate that the proposed method achieves improved performance for 3D model retrieval and recognition tasks.

Content-based 3D model retrieval using a single depth image from a low-cost 3D camera

In this paper, we propose an efficient method for 3D model retrieval using a single depth image. Unlike existing algorithms that use a complete 3D model or a user sketch as input queries, a single depth image is used as an input query, which can be captured easily with an off-the-shelf lowcost 3D camera, such as a Kinect camera. 3D models in the database are represented by multiple depth images acquired from adaptively sampled viewpoints. The proposed algorithm can retrieve relevant 3D models while considering local 3D geometric characteristics using a rotation-invariant feature descriptor. The proposed method consists of three steps: preprocessing, multiple depth image based representation (M-DIBR), and description of 3D models, and similarity measurement and comparison. Experimental results demonstrate that the proposed algorithm is convenient to use and its performance is comparable to recent algorithms in terms of retrieval accuracy and speed.

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.

A novel 3D model retrieval approach using combined shape distribution

With the rapid development of 3D digital shape information, content-based 3D model retrieval has become an important research field. 3D models are likely to be as prevalent as other multimedia data types in the future. There is a pressing need for effective content-based 3D model retrieval methods. In this paper, a novel combined shape distribution (CSD) descriptor is proposed for 3D model retrieval based on principal plane analysis and group integration. Firstly, based on principal plane analysis, the second principal plane is obtained by using sequential quadratic programming. Secondly, two novel 3D shape descriptors are proposed by introducing the plane normal vectors to other shape distributions. Thirdly, since the histogram of the proposed descriptors can be classified as belonging to one of three types: positive, negative, or crossed with each principal plane, further improvements to the descriptors are presented by integrating these three types of histograms. Finally, a CSD descriptor based on the synthesis of the above descriptors is proposed. Several retrieval performance measures and visual experimental results show that the new methods achieved good retrieval performance.

Shape-based retrieval and analysis of 3D models using fuzzy weighted symmetrical depth images

With the rapid development of 3D scanners, graphic accelerated hardware and 3D modeling tools, the application of 3D model databases is growing in both numbers and sizes, e.g. 3D body scans, head model and virtual mannequins. There is a pressing need for effective content-based 3D model retrieval methods. In this paper, a novel 3D model retrieval approach is proposed by using the Fuzzy Weighted Symmetrical Depth Images (FW-SDI). Firstly, three symmetrical planes of the 3D model are obtained based upon principal plane analysis and sequential quadratic programming, which are the distinctive characteristics of 3D model and perpendicular to each other. Secondly, three novel depth Images (NDI) and three depth differential images (DDI) are extracted by projecting the 3D surface to the proposed symmetrical planes. The Fourier descriptors of the novel depth Images and depth differential images are calculated. Finally, a fuzzy weighted procedure is conducted for combining the Fourier descriptors of NDI and DDI. Experiment results show that the proposed method can achieve better retrieval performance than others.

English

With the development of graphic accelerated hardware and 3D modeling tools, 3D models will be as prevalent as other multimedia data in the future. Thus, effective content-based 3D model retrieval systems are required for emerging needs. Many 3D model retrieval methods have been proposed in recent years. Shape distributions showed superiority over others due to rotation invariance and ease of computation, but the discriminative accuracy is limited for any loss in information. Fuzzy Shape Distributions (FSD) is proposed to improve the retrieval performance of distribution-based methods. First, two improved shape distributions are presented by using the concentric partition and symmetrical partition for 3D models. Second, the two enhanced descriptors are combined with a fuzzy weighted procedure. Experimental results show that the proposed FSD can achieve better retrieval performance.   Key words: 3D model retrieval, fuzzy weighted, shape distribution, sequential quadratic programming, content-based.

Using Depth Image in 3D Model Retrieval System

For content-based 3D model retrieval, an improved depth image-based feature extraction algorithm is proposed. First, a 3-D model is preprocessed. Secondly, six depth images are generated in three principal directions in the normalized coordinate system. Thirdly, the eigenvectors of 3D model are obtained through 2D Fourier Transform of the depth images. Finally a new method is used for low-frequency sampling. Experiments show that the approach performed quite well despite its apparently simple approach. In our large 3D database, our approach is well for variant resolution models and holds satisfied computational costs.

AN EFFICIENT 3D MODEL RETRIEVAL BASED ON PRINCIPAL AXES ANALYSIS AND FEATURE INTEGRATION

Three-dimensional (3D) model retrieval has gathered great importance in recent years, since the number of available 3D models on the Internet has drastically increased. Many content-based 3D model retrieval approaches have been proposed. Among these methods, visual similarity-based methods have shown higher retrieval accuracy. However, because these methods capture enormous shape features from different viewpoints or locations, a large amount of calculation and comparison is necessary. Furthermore, there is a trade-off between retrieval accuracy and speed. In this paper, a 3D model retrieval method constituting Continuous Principal Component Analysis (CPCA), Fourier descriptors, and Zernike moments is proposed. CPCA is applied to extract significant shape features based on projecting the model along the principal axes. Then, Fourier descriptors and Zernike moments are used to provide shape descriptors with rotation invariants. In addition, a feature integration process combines them. A strategy of similarity measure is proposed to solve the axes switching problem. To conclude, the experimental results show that the approach outperforms SECTORS2 and D2,18 and has slightly better retrieval results than Light Field Descriptor (LFD)6 and spin-image signatures.3 Moreover, the approach is more efficient and the storage size is much less.

ModelSeek: an effective 3D model retrieval system

Since object similarity is a subjective matter, the gap between low-level feature representations and high-level semantic concepts is a major problem in the field of content-based 3D model retrieval. This paper presents a novel composite model descriptor, which takes into account both visual and geometric characteristics of 3D models. It also proposes an original mapping mechanism from low-level model features to high-level semantic concepts based on the user's retrieval history, and so this method belongs to long-term relevance feedback algorithms for 3D model retrieval. Finally, an effective 3D model retrieval system "ModelSeek" has been built, and implemented with the introduced model descriptor and mapping mechanism. The experimental results show that the approaches above not only have significantly improved the retrieval performance, but have also achieved better retrieval effectiveness than the state-of-the-art techniques on the publicly available 3D model criterion that Princeton Shape Benchmark (PSB) and several standard evaluation measures.

A Survey of Content-Based 3D Model Retrieval with Semantic Features

Recent researches shows shape features is not enough for Content-based 3D retrieval. More and more researches are focusing on 3D retrieval by semantic features. This paper gives a full review of recent work about semantic-based 3D retrieval. Firstly,it makes an analysis about the existing researches,including extraction of semantic feature,active learning for semantic retrieval and semantic related retrieving platform. Secondly,this paper gives a comparison of some typical researches and concludes their contributions as well as limitations. Finally,some challenging research topics are proposed as a future work.

Content-based similarity for 3D model retrieval and classification

With the rapid development of 3D digital shape information, content-based 3D model retrieval and classification has become an important research area. This paper presents a novel 3D model retrieval and classification algorithm. For feature representation, a method combining a distance histogram and moment invariants is proposed to improve the retrieval performance. The major advantage of using a distance histogram is its invariance to the transforms of scaling, translation and rotation. Based on the premise that two similar objects should have high mutual information, the querying of 3D data should convey a great deal of information on the shape of the two objects, and so we propose a mutual information distance measurement to perform the similarity comparison of 3D objects. The proposed algorithm is tested with a 3D model retrieval and classification prototype, and the experimental evaluation demonstrates satisfactory retrieval results and classification accuracy.

A method for content-based 3D model retrieval by 2D projection views

An effective approach for 3D model retrieval is to represent models by a set of projection views. In this paper, we propose a flexible framework for view-based 3D model retrieval. The framework pro...

A powerful relevance feedback mechanism for content-based 3D model retrieval

The technique of relevance feedback has been introduced to content-based 3D model retrieval, however, two essential issues which affect the retrieval performance have not been addressed. In this pa...

Content-Based 3-D Model Retrieval: A Survey

As the number of available 3D models grows, there is an increasing need to index and retrieve them according to their contents. This paper provides a survey of the up-to-date methods for content-based 3D model retrieval. First, the new challenges encountered in 3D model retrieval are discussed. Then, the system framework and some key techniques of content-based 3D model retrieval are identified and explained, including canonical coordinate normalization and preprocessing, feature extraction, similarity match, query representation and user interface, and performance evaluation. In particular, similarity measures using semantic clues and machine learning methods, as well as retrieval approaches using nonshape features, are given adequate recognition as improvements and complements for traditional shape-matching techniques. Typical 3D model retrieval systems and search engines are also listed and compared. Finally, future research directions are indicated, and an extensive bibliography is provided.

DISCRIMINATIVE SPHERICAL WAVELET FEATURES FOR CONTENT-BASED 3D MODEL RETRIEVAL

The description of 3D shapes using features that possess descriptive power and are invariant under similarity transformations is one of the most challenging issues in content-based 3D model retrieval. Spherical harmonics-based descriptors have been proposed for obtaining rotation invariant representations. However, spherical harmonic analysis is based on a latitude-longitude parameterization of the sphere which has singularities at each pole, and therefore, variations of the north pole affect significantly the shape function. In this paper we discuss these issues and propose the usage of spherical wavelet transforms as a tool for the analysis of 3D shapes represented by functions on the unit sphere. We introduce three new descriptors extracted from the wavelet coefficients, namely: (1) a subset of the spherical wavelet coefficients, (2) the L1 and, (3) the L2 energies of the spherical wavelet sub-bands. The advantage of this tool is threefold; First, it takes into account feature localization and local orientations. Second, the energies of the wavelet transform are rotation invariant. Third, shape features are uniformly represented which makes the descriptors more efficient. Spherical wavelet descriptors are natural extensions of spherical harmonics and 3D Zernike moments. We evaluate, on the Princeton Shape Benchmark, the proposed descriptors regarding computational aspects and shape retrieval performance.