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3d model retrieval and pose estimation for indoor images by simulating scene context

GRAPHICAL modelS 2019年第0期103卷 101032-000页

作者： Liu, Mingming Guo, Jie Guo, Yanwen Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Sci & Technol Informat Syst Engn Lab Nanjing Jiangsu Peoples R China

In this paper we propose a single image-based 3d model retrieval and pose estimation method for indoor scenes. By simulating the scene context of the input image, our method is able to handle several challenging scenarios featuring cluttered backgrounds and severe occlusions. To use our system, the user only needs to drag a few semantic bounding boxes for the query objects. The proposed approach then retrieves the most similar 3d models from the ShapeNet model repository, and aligns them with the corresponding objects automatically. This requires that the 3d models are represented by calibrated view-dependent visual elements learned from the rendered views. With the estimated occlusion relationships, the rendered view images are stacked at the corresponding locations to simulate the scene context. By conducing matching between these composed scenes and the input image, the most similar 3d models under the approximate poses are retrieved. Experimental results on public datasets demonstrate the effectiveness of our proposed method. Moreover, we show that the retrieving time can be significantly reduced based on a novel greedy algorithm. Extensive experiments on synthesized images show that the proposed algorithm achieves a high retrieval accuracy, outperforming state-of-the-art methods.

关键词： 3d model retrieval Pose estimation Occlusion relationship estimation Scene context Cluttered background

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3d model retrieval using constructive-learning for cross-model correlation

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NEUROCOMPUTING 2018年 275卷 1-9页

作者： Yang, Jianbai Zhao, Jian Sun, Qiang Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Jilin Peoples R China

With the advance of 3d technology and digital image processing technique, there have been a great number of applications of 3d models, such as virtual reality, computed aided design, and entertainment. Under such circumstance, much research attention has been spent on 3d model retrieval in recent decades. Although extensive research efforts have been dedicated to this task, it is a difficult task to explore the correlation among 3d models, which is the key issue in 3d model retrieval. In this paper, we design and implement a constructive-learning for cross-model correlation algorithm for 3d model retrieval. In this method, we first extract view features from multi-views of 3d models. To exploit the cross-model correlation, we formulate the correlation of 3d models in a hypergraph structure, where both the vertex correlation and the edge correlation are simultaneously learned in a constructive-learning process. Then, the correlation of each model to the query can be used for retrieval. To justify the performance of our proposed algorithm, we have implemented the method and tested on two datasets. We have compared it with recent state-of-the-art methods, and the results have shown superior performance of our proposed method. (c) 2017 Published by Elsevier B.V.

关键词： 3d model retrieval Cross-model correlation Constructive-learning

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3d model retrieval via single image based on feature mapping

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MULTIMEdIA TOOLS ANd APPLICATIONS 2018年第17期77卷 22051-22069页

作者： Liu, Anan Liu, Nannan Nie, Weizhi Su, Yuting Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China

With the development of manufacture, more and more 3d models are generated by users and many differnet factories. 3d model retrieval has been receiving more and more attention in computer vision and the field of data analysis. In this paper, we propose a novel 3d model retrieval algorithm by cross-modal feature mapping (CMFM), which utilize one single image as query information to address 3d model retrieval problem. Specifically, in this paper, we first proposed to leverage 2d image to handle 3d model retrieval problem, which is one new problem in this field. The proposed feature learning method can benefit: 1) avoiding the interference of query image recorded by different visual sensor;2) handling cross-modal data retrieval by simple computer vision technologies, which can guarantee the performance of retrieval and also control that the retrieval time hold a low level;3) the low complexity of this method can guarantee that this method can be applied in many fields. Finally, we validate the retrieval method on three popular datasets. Extensive comparison experiments show the superiority of the proposed mehtod. To the best of our knowledge, it is the first method to handle 3d model retreival based on one single 2d image.

关键词： 3d model retrieval View-based Feature mapping/learning Iteration optimization CNN

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CHARACTERISTIC VIEWS EXTRACTION MOdAL BASEd-ON dEEP REINFORCEMENT LEARNING FOR 3d model retrieval 26

CHARACTERISTIC VIEWS EXTRACTION MODAL BASED-ON DEEP REINFORC...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Nie, Weizhi Wang, Weijie Liu, Anan Chen, Chuang Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538662496

In this paper, we propose an effective framework for view-based 3d model retrieval based on reinforcement learning, which aims to extract the most important views from the view set of 3d models for model representation. Since the choice of the representative views can be treated as a Markov decision process (MdP), we model the view extraction as a progressive process through deep reinforcement learning. First, the visual tool developed by OpenGL is utilized to extract 12 rendered pictures in order from a 3d model. Second, these 12 views are fed into the network as a view-based sequence, and then we utilize characteristic views extraction model to extract the most informative views of the 3d model. Finally, a similarity measure between 3d models is performed to complete the last step pf the model retrieval problem. In the experiment section, the classic modelNet40 is utilized to evaluate the performance of the proposed method. Extensive experiments and corresponding experimental results have demonstrated the superiority of our approach.

关键词： Reinforcement learning view-based 3d model retrieval

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

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MULTIMEdIA TOOLS ANd APPLICATIONS 2018年第2期77卷 2921-2944页

作者： Li, Yuhua Lei, Haopeng Lin, Shujin Luo, Guoliang Zhengzhou Univ Light Ind Software Engn Coll Zhengzhou 450001 Henan Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Sun Yat Sen Univ Sch Commun & Design Guangzhou 510006 Guangdong Peoples R China Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China

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.

关键词： 3d model retrieval Composite feature Filter Global feature Local feature

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

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MULTIMEdIA TOOLS ANd APPLICATIONS 2018年第3期77卷 3229-3243页

作者： Liu, An-An Shi, Yang Nie, Wei-Zhi Su, Yu-Ting Tianjin Univ Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Tianjin Peoples R China

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.

关键词： 3d model retrieval Multi-view Feature learning Feature dimensionality reduction SVd Zernike moments

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View-Based 3d model retrieval via Multi-graph Matching

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NEURAL PROCESSING LETTERS 2018年第3期48卷 1395-1404页

作者： Nie, Weizhi Liu, Anan Hao, Yahui Su, Yuting Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

With the rapid development of 3d technology, 3d model retrieval has attracted a large amount of interest in computer vision field. In this paper, we propose a composition-based multi-graph matching method in this paper. Firstly, compute the pairwise matching affinity one-to-one graph matching. Secondly, seek the optimal intermediate graph by diverse graph matching orders, according to the consistency of global matching. Finally, the classic optimization method is used to get the best matching result for similarity measurement. We validate our approach using ETH, NTU and MV-REd 3d model datasets with convolutional neural network features. Extensive experiments show the superiority of the proposed method.

关键词： 3d model retrieval View-based Multi-graph matching CNN

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Multi-scale CNNs for 3d model retrieval

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MULTIMEdIA TOOLS ANd APPLICATIONS 2018年第17期77卷 22953-22963页

作者： Nie, Weizhi Xiang, Shu Liu, Anan Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

Recent advancements in low-cost 3d sensors and mobile devices of virtual 3d models have additionally facilitated the accessibility of 3d data. 3d model retrieval is becoming an indispensable function for modern search engines. An effective retrieval model is at the core of computer vision. With the continuous improvement of 3d data, there are large number of methods to solve this problem. Existing works proposed numerous works to deal with feature extraction and object matching. Most of them are unable to fully exploit the information of 3d representations. To address this problem, we propose a novel multi-layer deep network in this paper. First, multiple rendered images are extracted from a 3d object, and combined into one representative view, which is the actual input of the network. Then, the novel multi-layer network structure is trained and tested on these representative views, generating the feature leaning model, which owns the local and global information of a 3d object. Finally, simple Euclidean metric is used to compute the similarity between two different 3d models to complete the retrieval problem. Extensive experiments and corresponding experimental results have demonstrated the superiority of our approach.

关键词： 3d model retrieval CNN multi-view Multi-scale

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Sketch-Based 3d model retrieval Using Attributes

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INTERNATIONAL JOURNAL OF GRId ANd HIGH PERFORMANCE COMPUTING 2018年第3期10卷 60-75页

作者： Lei, Haopeng Luo, Guoliang Li, Yuhua Liu, Jianming Ye, Jihua Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China Jiangxi Normal Univ Sch Software Nanchang Jiangxi Peoples R China Zhengzhou Univ Light Ind Software Engn Coll Zhengzhou Henan Peoples R China

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.

关键词： 3d model retrieval Low-Level Feature Semantic Attributes Visual Words

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A semantic recommender algorithm for 3d model retrieval based on deep belief networks

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE ANd ENGINEERING 2019年第1期19卷 93-103页

作者： Chen, Li Liu, Hong Moore, Philip Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China Shandong Normal Univ Shandong Prov Key Lab Novel Distributed Comp Soft Sch Informat Sci & Engn Jinan Shandong Peoples R China

Interest in 3d modelling is growing, however, the retrieval results achieved for semantic-based 3d model retrieval systems have been disappointing. In this paper, we propose a novel semantic recommendation algorithm based on a deep belief network (dBN-SRA) to implement semantic retrieval with potential semantic correlations [between models] being achieved using deep learning from known model samples. The algorithm uses the feature correlation [between the models] as the conditions to enable semantic matching of 3d models to obtain the final recommended retrieval result. Our proposed approach has been shown to improve the effectiveness of 3d model retrieval in terms of both retrieval time and importantly accuracy. Additionally, our study and our reported results suggest that our posited approach will generalise to recommender systems in other domains which are characterised by multiple feature relationships.

关键词： deep belief network dBN 3d model retrieval recommender algorithm cluster analysis

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