3D Model Retrieval Method Research Articles

View-based 3D model retrieval via supervised multi-view feature learning

With the development of the processing technologies of 3D model and the increasing of 3D model in different application flieds, 3D model retrieval is attracting more and more people’s attention. In order to handle this problem, most of approaches focus on the feature extraction form different virtual view. It is hard to guarantee the robustness and also ignore the correlation between both views. Thus, we propose an effective view-based 3D model retrieval method via supervised multi-view feature learning (SMFL). First, the subspace dimension of viusal feature is generated through Singular Value Decomposition (SVD) algorithm. This step is used to select main information from multi-view in order to reduce the final amount of calculation; Secondly, we consider the relationship of multi-view from same class and the correlation between two different classes to make the feature mapping in order to reduce the different of views from the same class and increase the different of views from the difference class; Finally, the projection mapping corresponding to the inner product of each 3D model helps to calculate the similarities between two different 3D models. The extensive experiments are conducted on popular ETH, NTU, MV-RED and PSB 3D model datasets with Zernike moments. The comparative results or The experimental results with existing 3D model retrieval methods show the superiority of the proposed method.

A new sketch-based 3D model retrieval method by using composite features

With the rapid growth of available 3D models in various areas, effective methods to search 3D models are becoming increasingly important. In this paper, we propose a new method for sketch-based 3D model retrieval. Different from current methods that make use of either global or local features, the proposed method uses composite features combining global and local features extracted from representative 2D views of 3D models. The global features, shape strings, represent exterior boundary shape of the views and the local features, improved Pyramid of Histograms of Orientation Gradients (iPHOG), represent their interior details. Specifically, a global feature based filtering step is adopted to select more relevant candidate models to the query sketch and a local feature based process is used to refine chosen candidates. To evaluate the performance of the proposed method with that of other previous ones, we conducted a series of experiments on public standard 3D model databases. Experimental results are presented and indicate the effectiveness of the new approach for sketch-based 3D model retrieval.

An effective freeform surface retrieval approach for potential machining process reuse

With the increasing of the machining process data, which are the direct and effective carrier of knowledge, intelligence and experience of skilled engineers, machining process data-driven intelligent machining process planning is becoming more and more important in manufacturing industries. One of the key technologies is to retrieve the similar geometry with machining process reuse value in a fine manner. However, existing 3D CAD model retrieval methods for manufacturing reuse mainly focus on the parts composed of non-freeform surface features machined with 2 1/2-axis CNC milling, while the parts including complex freeform surfaces accounted for a large proportion are little involved. In this paper, a novel freeform surface retrieval approach for potential machining process reuse is presented. First, similar tensor field pattern freeform surface feature is introduced to represent the complex freeform surface into structured freeform surface model. Then, freeform surface content code for accelerating freeform surface retrieval is given to filter out unmatched freeform surfaces efficiently. Moreover, the principal pathline indicating the overall evolution trend of feature pathlines is extracted and represented using D2 shape descriptor to establish the feature similarity assessment model. Finally, sub-graph isomorphism-based matched feature pairs extraction algorithm is presented to calculate the similarity between matched freeform surfaces. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

3D models retrieval algorithm based on multimodal data

With the development of computer vision in recent year, 3D models have been utilized in many applications, such as virtual reality, medical surgical, geographic information system. With the growth of 3D models, it is necessary to develop effective 3D model retrieval methods for data management. In this paper, we proposed a novel algorithm based on multimodal 3D model data to handle model retrieval problem. First, we extract structure information and visual information from each virtual 3D model. Then, a universal graph matching is employed to handle similarity measure in different modals respectively. Finally, a simple statistical model is utilized to handle similarity measure and finish retrieval process. The final comparing experiments demonstrate the superiority of our approach.

3D object retrieval based on multi-view convolutional neural networks

Recently, 3D objects have been widely designed and applied in various technical applications. In this paper, we propose a novel 3D model retrieval method based on Multi-View Convolutional Neural Networks (MVCNN). By integrating visual information from multiple views, we construct a composite CNN structure to generate single terse descriptor with powerful discrimination for individual 3D object. Our method can benefit from the hidden relevance of visual information in deep structure. Instead of computing similarities between each pair of view-feature, we only need to measure the comparability of two object once, which brings high efficiency. Moreover, this method can avoid camera constraint when capturing multi-view representation. Extensive experiments on NTU and ITI datasets can support the superiority of the proposed method.

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.

A non-rigid 3D model retrieval method based on scale-invariant heat kernel signature features

The number of non-rigid 3D models increases steadily in various areas. It is imperative to develop efficient retrieval system for 3D non-rigid models. As we know, global features fail to consistently describe the intra-class variability of non-rigid 3D models, the local features are more effective than global features for the retrieval of non-rigid 3D models. In this paper, we use Heat Kernel Signature (HKS) as the local features to represent non-rigid 3D models and further propose the retrieval method based on scale-invariant local features. Firstly, we extract key-points at multiple scales automatically. Then, the HKS local features are computed for each key-point. However, the HKS features are sensitive to scale. In order to solve this problem, we convert the scale problem into the translation problem using the diffusion Wavelets transform. To solve the translation problem, we use a kind of histogram equalization technique. Finally, we use the bipartite graph matching algorithm to compute similarity between the 3D models. Experimental results on two public benchmarks show that our method outperforms state-of-the-art methods for non-rigid 3D models retrieval.

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.

Efficient semi-supervised multiple feature fusion with out-of-sample extension for 3D model retrieval

Multiple visual features have been proposed and used in 3-dimensional (3D) model retrieval in recent years. Since each visual feature reflects a unique characteristic about the model, they have unequal discriminative power with respect to a specific category of 3D model, and they are complementary to each other in model representation. Thus, it would be beneficial to combine multiple visual features together in 3D model retrieval. In light of this, we propose an efficient Semi-supervised Multiple Feature Fusion (SMFF) method for view-based 3D model retrieval in this paper. Specifically, We first extract multiple visual features to describe both the local and global appearance characteristics of multiple 2D projected images that are generated from 3D models. Then, SMFF is adopted to learn a more compact and discriminative low-dimensional feature representation via multiple feature fusion using both the labeled and unlabeled 3D models. Once the low-dimensional features have been learned, many existing methods such as SVM and KNN can be used in the subsequent retrieval phase. Moreover, an out-of-sample extension of SMFF is provided to calculate the low-dimensional features for the newly added 3D models in linear time. Experiments on two public 3D model datasets demonstrate that using such a learned feature representation can significantly improve the performance of 3D model retrieval and the proposed method outperforms the other competitors.

A novel sketch-based 3D model retrieval method by integrating skeleton graph and contour feature

With the rapid growth of 3D models on the Web, effective methods to retrieve appropriate 3D models are becoming crucial. In this paper, we propose a novel sketch-based 3D model retrieval approach which integrates the skeleton graph and contour feature. Skeleton graph represents the topology structure of the query sketch as well as representative 2D views of 3D models, and contour feature captures their detailed information. Moreover, A hierarchical coarse-to-fine searching strategy is employed in this method. Firstly, a skeleton graph based matching method is used to eliminate those 3D models which are dissimilar to the query sketch. Then, the selected candidate models are refined by measuring the similarity of their contour feature. In addition, we adopt a graph transduction method to improve the ranking order of the retrieved 3D models. The proposed method was tested on the public sketch benchmarks and compared with other leading sketch-based 3D model retrieval methods. The experiment results demonstrate that our approach can accurately retrieve desired 3D models for users and has a superior performance to other state-of-the-art approaches.

An Invariant Electronic Descriptor for 3D Model Retrieval

With the rapid development of 3D scanners, graphic accelerated hardware and modeling tools, the use of 3D model databases throughout the Internet is growing, and this trend should continue. Thus, there is an urgent demand for effective 3D model retrieval methods and systems. In this paper, a novel rotation invariant and effective 3D object retrieval approach called electronic descriptor is presented, which is a combined shape distribution by considering the randomly selected points on 3D surface as electrons. Experimental results show that the proposed method is superior to other shape distribution based methods.

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.

3D CAD Model Representation and Retrieval based on Hierarchical Graph

Due to 3D CAD models are often characterized with complicated geometry and topology, how to help the designers quickly and accurately find the object models from the database containing massive amount of models using the relatively rough query instance is still a big challenge. In this paper, a novel representation of 3D CAD models using hierarchical graph (HG) is proposed. The model descriptors are divided into shape feature descriptors and topology relationship descriptors. These two descriptors can be extracted from HG. In this way, coarse-grained and fine-grained 3D CAD model retrieval are implemented. On this basis, a 3D model retrieval method is proposed based on genetic algorithm (GA) and ant colony optimization (ACO), which are employed to detect the common sub-graph in the corresponding the hierarchical graphs of different models. This method can improve the accuracy and efficiency of 3D CAD model retrieval. Based on the above researches, a 3D model retrieval system HUST-CMRS is developed. Our experimental results show that, the algorithm proposed in this paper can implement multi-mode indexing and satisfy personalized need of users.

3D CAD Model Retrieval Driven Rapid NC Programming Method for NC Process Reuse

To realize the effective reuse of numerical control(NC) machining process, a 3D CAD model retrieval driven rapid NC programming method for NC machining process reuse is presented. A feature-based machining process model is elaborated to effectively represent the existing NC machining process. 3D CAD model retrieval method based on manufacturing semantics is introduced to realize the similarity assessment between manufacturing features. Two NC machining process reuse modes are proposed to realize the rapid NC programming according to manufacturing feature similarities. The approach proposed is implemented and tested by hundreds of mechanical parts in the phototype system. Preliminary results show that the method can effectively realize the reuse of NC machining process, and the efficiency can meet the requirement of engineering application.

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.

3D model retrieval method with multi-feature fusion based on semantic matrix feedback

3D model retrieval method with multi-feature fusion based on semantic matrix feedback

Combining color and shape descriptors for 3D model retrieval

Nowadays many three dimensional models feature color information together with the shape description. However current content-based retrieval schemes for 3D models are based on shape information only and ignore color clues. The significance of shape vs. color clues for 3D model retrieval is instead a fundamental issue still almost unexplored at this time. A possible approach is to extend shape-based 3D model retrieval methods of proven effectiveness in order to include color. This work follows such rationale and introduces an extended version of the spin-image descriptor that can account also for color data. The comparison of color descriptors is performed using a novel scheme that allows to recognize as similar also objects with different colors but with the same color distribution over the shape. Shape and color similarity are finally combined together by an algorithm based on fuzzy logic. Experimental results show how the joint use of color and shape data allows to obtain better results than each of the two types of information alone. Comparisons with state-of-the-art content-based retrieval methods for 3D models also show how the proposed scheme outperforms standard solutions on object classes with meaningful color information.

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.

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...