Cube of Space Sampling for 3D Model Retrieval

Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method.

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Sketch-Based 3D Model Retrieval Using Attributes

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2018070105 ◽

2018 ◽

Vol 10 (3) ◽

pp. 60-75 ◽

Cited By ~ 1

Author(s):

Haopeng Lei ◽

Guoliang Luo ◽

Yuhua Li ◽

Jianming Liu ◽

Jihua Ye

Keyword(s):

3D Model ◽

3D Models ◽

3D Model Retrieval ◽

Superior Performance ◽

Visual Features ◽

Low Level ◽

Feature Descriptors ◽

Model Retrieval ◽

Sketch Retrieval ◽

Semantic Attributes

With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.

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An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network

Complexity ◽

10.1155/2020/9050459 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Bo Ding ◽

Lei Tang ◽

Yong-jun He

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

3D Model ◽

3D Models ◽

3D Model Retrieval ◽

Similarity Matching ◽

Retrieval Method ◽

Model Classification ◽

Model Retrieval ◽

Index Building

Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. Next, the features are organized according to their labels to build indexes. In this stage, the views used for representing 3D models are reduced substantially on the premise of keeping enough information of 3D models. This method reduces the number of similarity matching by 87.8%. In retrieval, the 2D views of the input model are classified into a category with the CNN and voting algorithm, and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way, the searching space for retrieval is reduced. In addition, the number of used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model, the retrieval process will be terminated ahead of time. The variable view matching method further reduces the number of similarity matching by 21.4%. Experiments on the rigid 3D model datasets ModelNet10 and ModelNet40 and the nonrigid 3D model dataset McGill10 show that the proposed method has achieved retrieval accuracy rates of 94%, 92%, and 100%, respectively.

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A new sketch-based 3D model retrieval approach by using global and local features

Graphical Models ◽

10.1016/j.gmod.2013.11.002 ◽

2014 ◽

Vol 76 (3) ◽

pp. 128-139 ◽

Cited By ~ 17

Author(s):

Feng Wang ◽

Lanfen Lin ◽

Min Tang

Keyword(s):

3D Model ◽

Local Features ◽

3D Model Retrieval ◽

Model Retrieval ◽

Global And Local

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A New 3D Model Retrieval Method with Building Blocks

International Journal of Computer Games Technology ◽

10.1155/2009/572030 ◽

2009 ◽

Vol 2009 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Mingquan Zhou ◽

Qingsong Huo ◽

Guohua Geng ◽

Xiaojing Liu

Keyword(s):

3D Model ◽

Building Blocks ◽

3D Models ◽

3D Model Retrieval ◽

Virtual Space ◽

3D Data ◽

Model Retrieval ◽

Voxel Model ◽

Interactive 3D ◽

Application Fields

As the numbers of 3D models available grow in many application fields, there is an increasing need for a search method to help people find them. Unfortunately, traditional search techniques are not always effective for 3D data. In this paper, we describe a novel method of interactive 3D model retrieval with building blocks. First, by using a cube block as the baseblock in a 3D virtual space, we may construct the query model with human-computer interaction method. Then through retrieving the polygon model of the database generated by the voxel model, we may get retrieval results in real time. Experiments are conducted to evaluate the performance of the proposed method.

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Deep Common Semantic Space Embedding for Sketch-Based 3D Model Retrieval

Entropy ◽

10.3390/e21040369 ◽

2019 ◽

Vol 21 (4) ◽

pp. 369 ◽

Cited By ~ 2

Author(s):

Jing Bai ◽

Mengjie Wang ◽

Dexin Kong

Keyword(s):

Visual Perception ◽

Computer Aided Design ◽

3D Model ◽

Semantic Content ◽

3D Models ◽

Semantic Space ◽

3D Model Retrieval ◽

Novel Approach ◽

Model Retrieval ◽

Aided Design

Sketch-based 3D model retrieval has become an important research topic in many applications, such as computer graphics and computer-aided design. Although sketches and 3D models have huge interdomain visual perception discrepancies, and sketches of the same object have remarkable intradomain visual perception diversity, the 3D models and sketches of the same class share common semantic content. Motivated by these findings, we propose a novel approach for sketch-based 3D model retrieval by constructing a deep common semantic space embedding using triplet network. First, a common data space is constructed by representing every 3D model as a group of views. Second, a common modality space is generated by translating views to sketches according to cross entropy evaluation. Third, a common semantic space embedding for two domains is learned based on a triplet network. Finally, based on the learned features of sketches and 3D models, four kinds of distance metrics between sketches and 3D models are designed, and sketch-based 3D model retrieval results are achieved. The experimental results using the Shape Retrieval Contest (SHREC) 2013 and SHREC 2014 datasets reveal the superiority of our proposed method over state-of-the-art methods.

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A Novel 3D Model Retrieval Approach Based on Model-Partitioning and Fuzzy Relevance Feedback

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.186 ◽

2010 ◽

Vol 143-144 ◽

pp. 186-190

Author(s):

Kuan Sheng Zou ◽

Chun Ho Wu ◽

Wai Hung Ip ◽

Ching Yuen Chan ◽

Kei Leung Yung ◽

...

Keyword(s):

3D Model ◽

Retrieval System ◽

3D Models ◽

3D Model Retrieval ◽

Retrieval Performance ◽

System A ◽

Model Retrieval ◽

Euclidean Motion ◽

Feedback Method ◽

Model Partitioning

3D models play an important role in many applications, so there is an urgent need for an effective content based 3D model retrieval system. A variety of 3D model retrieval methods have been proposed in recent years. Shape distributions show superiority over other methods due to ease of computation and invariance to Euclidean motion, but there is poor retrieval performance for the loss in information. This paper introduces two model-partitioning methods to improve shape distributions, in which the two enhanced descriptors are combined with a fuzzy feedback method. Experimental results show that the proposed methods can achieve better retrieval performance.

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3D Model Retrieval Based on Distance Classification Histogram

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.733.931 ◽

2015 ◽

Vol 733 ◽

pp. 931-934 ◽

Cited By ~ 2

Author(s):

Ji Lai Zhou ◽

Ming Quan Zhou ◽

Guo Hua Geng

Keyword(s):

3D Model ◽

3D Models ◽

3D Model Retrieval ◽

Experimental Results ◽

Model Surface ◽

Model Retrieval

This paper presents a new algorithm to retrieve 3D model on distance classification histogram. First, we select the certain number of random points on the model surface and compute the distance between two random points. Secondly, we sort the distance into two types which is based on the different geometry properties of these distance and construct the distance classification histogram. Finally, we measure the similarity of 3D models by comparing distance classification histogram. The experimental results on PSB show that our method has a good performance in precision and computational complication.

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3D Model Retrieval Based on Fuzzy Weighted Shape Distributions

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.201-203.1678 ◽

2011 ◽

Vol 201-203 ◽

pp. 1678-1681 ◽

Cited By ~ 1

Author(s):

Zeng Qiang Chen ◽

Kuan Sheng Zou ◽

Wai Hung Ip ◽

Ching Yuen Chan

Keyword(s):

3D Model ◽

3D Models ◽

Total Surface Area ◽

3D Model Retrieval ◽

Retrieval Algorithm ◽

Shape Functions ◽

Shape Distribution ◽

Model Retrieval ◽

The Mean ◽

Fuzzy Weights

Shape distribution is considered as a kind of art state 3D model retrieval algorithm due to its simplicity, robustness, and not need model pretreatment. Its disadvantage is that the retrieval precision is not high enough. Despite the introduction of five kinds of shape functions, each of which can not sufficiently expresse the 3D models. This paper uses the D2 distribution, the improved D1 distribution and the total surface area method to retrieve 3D models respectively. Then give weights to each method after unitary them. Set fuzzy rules are set to decide the fuzzy weights of three methods according to the mean proportion and variance proportion, Experimental results show that this algorithm can improve the retrieval results significantly.

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