A Comparative Survey on 3D Models Retrieval Methods

Pattern Recognition, 2013

Non-rigid 3D shape retrieval has become an active and important research topic in contentbased 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using 6 commonly-utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].

Multimedia Tools and Applications, 2013

Non-rigid and partial 3D model retrieval are two significant and challenging research directions in the field of 3D model retrieval. Little work has been done in proposing a hybrid shape descriptor that works for both retrieval scenarios, let alone the integration of the component features of the hybrid shape descriptor in an automatic way. In this paper, we propose a hybrid shape descriptor that integrates both geodesic distance-based global features and curvature-based local features. We also develop an automatic algorithm to generate meta similarity resulting from different component features of the hybrid shape descriptor based on Particle Swarm Optimization. Experimental results demonstrate the effectiveness and advantages of our framework, as well as the significant improvements in retrieval performances. The framework is general and can be applied to similar approaches that integrate more features for the development of a single algorithm for both non-rigid and partial 3D model retrieval.

2006

In this paper, we describe several 3D shape descriptors for 3D model retrieval and integrate them in order to obtain higher performance than single descriptor may yield. We analyze four feature vector (FV) integration approaches: Pure FV Integration (PFI), Reduced FV Integration (RFI), Distance Integration (DI), and Rank Integration (RI). We observe which weighting factor might be the best for each approach. Our experiments show that the weighting factors consistently enhance the retrieval performance on not only training dataset, but also another extended dataset. Our experiments also highlight that RFI, which is obviously useful for processing unknown query object, is the best among the others. In another side, DI provides faster processing as it uses pre-computed distance, but does not have a capability of processing unknown query object. Hence, both approaches could be combined in order to obtain higher efficiency and effectiveness of 3D model retrieval system for either known or unknown query object.

2006

This paper presents an approach to measure 3D similarity by combining two feature vectors. We extract the feature vectors by employing two similarity models: Direction Vector of Surfaces (DVS) and Shape Histogram of Projected Volume (SHV). Then we merge the features by two approaches: merging the two original feature vectors and merging computed-distances. Our experiments show that combining two features using either feature merging or distance merging enhances the retrieval performance. Furthermore, we show that employing weighting factor to the merging process implies differently to the retrieval performance, depending on data set distribution. Finally, we introduce an idea of meta feature-vectors which regards the already calculated distances as new feature vectors. Using this approach, a new similarity space might be established, and new distances could be calculated in order to enhance the performance.

2008

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

The objective of the Shape Retrieval Contest '09 (SHREC'09) of Partial Models is to compare the performances of algorithms that accept a range image as the query and retrieve relevant 3D models from a database. The use of a range scan of an object as the query addresses real life scenarios, where the task of the system is to analyze a 3D scene and to identify what type of objects are present in the scene. Another benefit of developing retrieval algorithms based on range scans of objects is that they enable a simple 3D search interface composed of a desktop 3D scanner. Two groups have participated in the contest and have provided rank lists for the query set that is composed of range scans of 20 objects. This paper presents descriptions of the participants' methods and the results of the contest.

Lecture Notes in Computer Science, 2008

Recently, content based 3D shape retrieval has been an active area of research. Benchmarking allows researchers to evaluate the quality of results of different 3D shape retrieval approaches. Here, we propose a new publicly available 3D shape benchmark to advance the state of art in 3D shape retrieval. We provide a review of previous and recent benchmarking efforts and then discuss some of the issues and problems involved in developing a benchmark. A detailed description of the new shape benchmark is provided including some of the salient features of this benchmark. In this benchmark, the 3D models are classified mainly according to visual shape similarity but in contrast to other benchmarks, the geometric structure of each model is modified and normalized, with each class in the benchmark sharing the equal number of models to reduce the possible bias in evaluation results. In the end we evaluate several representative algorithms for 3D shape searching on the new benchmark, and a comparison experiment between different shape benchmarks is also conducted to show the reliability of the new benchmark.

Communications of the Acm, 2005

This course covers concepts, methods, and applications for retrieving and analyzing 3D models in large databases. Emphasis is placed on geometric representations and algorithms for indexing and matching 3D objects based on their shapes. A survey of current shape descriptors, query interfaces, and shape-based retrieval applications will be included.

2010 Shape Modeling International Conference, 2010

This paper presents a novel 3D shape retrieval method, which uses Bag-of-Features and an efficient multiview shape matching scheme. In our approach, a properly normalized object is first described by a set of depth-buffer views captured on the surrounding vertices of a given unit geodesic sphere. We then represent each view as a word histogram generated by the vector quantization of the view's salient local features. The dissimilarity between two 3D models is measured by the minimum distance of their all (24) possible matching pairs. This paper also investigates several critical issues including the influence of the number of views, codebook, training data, and distance function. Experiments on four commonly-used benchmarks demonstrate that: 1) Our approach obtains superior performance in searching for rigid models. 2) The local feature and global feature based methods are somehow complementary. Moreover, a linear combination of them significantly outperforms the stateof-the-art in terms of retrieval accuracy.