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3d model retrieval based on multi-view attentional convolutional neural network

MULTIMEdIA TOOLS ANd APPLICATIONS 2020年第7-8期79卷 4699-4711页

作者： Liu, An-An Zhou, He-Yu Li, Meng-Jie Nie, Wei-Zhi Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

We propose a discriminative Multi-View Attentional Convolutional Neural Network, dubbed as MVA-CNN, which takes the multiple views of an shape as input and output the object category. Unlike previous view-based approaches that simply "compile" the view features into a compact 3d descriptors, our method can discover the context among multiple views in both the visual and spatial domain. First, we extract multiple rendered images from a 3d object by virtual cameras, and then we use Convolutional Neural Network (CNN) to abstract the information of the views. Second, we aggregate the visual views by two steps: 1). an element-wise maximum operation across the view features is adopted to discover discriminative features. 2). a soft attention mechanism is used to dynamically adjust the shape descriptors for better representing the spatial information. The entire network can be trained in an end-to-end way with the standard backpropagation. We verify the effectiveness of MVA-CNN on two widely used datasets: modelNet10, modelNet40 by comparing our method with state-of-the-art methods.

关键词： 3d model retrieval Multi-view CNN LSTM

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3d model retrieval Using Bipartite Graph Matching Based on Attention

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NEURAL PROCESSING LETTERS 2020年第2期52卷 1043-1055页

作者： Sun, Shanlin Li, Yun Xie, Yunfeng Tan, Zhicheng Yao, Xing Zhang, Rongyao Guilin Univ Aerosp Technol Coll Elect Informat & Automat Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Coll Informat & Commun Guilin 541004 Guangxi Peoples R China

In this paper, we propose an attention-based bipartite graph 3d model retrieval algorithm, where many-to-many matching method, the weighted bipartite graph matching, is employed for comparison between two 3d models. Considering the panoramic views can donate the spatial and structural information, in this work, we use panoramic views to represent each 3d model. Attention mechanism is used to generate the weight of all views of each model. And then, we construct a weighted bipartite graph with the views of those models and the weight of each view. According to the bipartite graph, the matching result is used to measure the similarity between two 3d models. We experiment our method on modelNet, NTU and ETH datasets, and the experimental results and comparison with other methods show the effectiveness of our method.

关键词： 3d model retrieval Bipartite graph matching Attention mechanism

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M-GCN: Multi-Branch Graph Convolution Network for 2d Image-based on 3d model retrieval

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IEEE TRANSACTIONS ON MULTIMEdIA 2021年 23卷 1962-1976页

作者： Nie, Wei-Zhi Ren, Min-Jie Liu, An-An Mao, Zhendong Nie, Jie Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Sci & Technol China Sch Elect Informat Engn Hefei 230052 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China

2d image based 3d model retrieval is a challenging research topic in the field of 3d model retrieval. The huge gap between two modalities - 2d image and 3d model, extremely constrains the retrieval performance. In order to handle this problem, we propose a novel multi-branch graph convolution network (M-GCN) to address the 2d image based 3d model retrieval problem. First, we compute the similarity between 2d image and 3d model based on visual information to construct one cross-modalities graph model, which can provide the original relationship between image and 3d model. However, this relationship is not accurate because of the difference of modalities. Thus, the multi-head attention mechanism is employed to generate a set of fully connected edge-weighted graphs, which can predict the hidden relationship between 2d image and 3d model to further strengthen the correlation for the embedding generation of nodes. Finally, we apply the max-pooling operation to fuse the multi-graphs information and generate the fusion embeddings of nodes for retrieval. To validate the performance of our method, we evaluated M-GCN on the MI3dOR dataset, Shrec 2018 track and Shrec 2014 track. The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

关键词： Three-dimensional displays Solid modeling Two dimensional displays Computational modeling Visualization Feature extraction Predictive models Cross-domain retrieval 3d model retrieval multi-head attention multiple graphs

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Hierarchical deep semantic alignment for cross-domain 3d model retrieval

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JOURNAL OF VISUAL COMMUNICATION ANd IMAGE REPRESENTATION 2023年第1期95卷

作者： Song, dan Ling, Yuting Li, Tianbao Wang, Teng Li, Xuanya Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Baidu Inc Beijing Peoples R China

With the development of deep learning and the widespread application of 3d modeling technology, image -based cross-domain 3d model retrieval has attracted more and more researchers' attention. Existing methods have achieved success by aligning the feature distributions from different domains. However, previous methods just statistically align the domain-level or class-level feature distributions, leaving sample discriminability a margin to be improved for retrieval. To address this issue, this paper proposes a Hierarchical deep Semantic Alignment Network (HdSAN) for cross-domain 3d model retrieval, which combines the proposed sample -level semantic enhancement with global domain alignment and class semantic alignment. Concretely, we adopt adversarial domain adaptation at the domain level and dynamically align the class centers of two domains at the class level. To further improve sample discriminability, we design intra-domain and cross -domain triplet center alignment to enhance the semantic representation ability at the sample level. Experiments on two commonly-used cross-domain 3d model retrieval datasets MI3dOR-1 and MI3dOR-2 demonstrate the effectiveness of the proposed method.

关键词： 3d model retrieval Unsupervised domain adaptation Representation learning

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Image-based 3d model retrieval via disentangled feature learning and enhanced semantic alignment

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INFORMATION PROCESSING & MANAGEMENT 2023年第2期60卷

作者： Nie, Jie Zhang, Ting Li, Tianbao Yu, Shusong Li, Xuanya Wei, Zhiqiang Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Baidu Inc Beijing 100105 Peoples R China

With the development of 3d technology and the increase in 3d models, 2d image-based 3d model retrieval tasks have drawn increased attention from scholars. Previous works align cross-domain features via adversarial domain alignment and semantic alignment. However, the extracted features of previous methods are disturbed by the residual domain-specific features, and the lack of labels for 3d models makes the semantic alignment challenging. Therefore, we propose disentangled feature learning associated with enhanced semantic alignment to address these problems. On one hand, the disentangled feature learning enables decoupling the twisted raw features into the isolated domain-invariant and domain-specific features, and the domain-specific features will be dropped while performing adversarial domain alignment and semantic alignment to acquire domain-invariant features. On the other hand, we mine the semantic consistency by compacting each 3d model sample and its nearest neighbors to further enhance semantic alignment for unlabeled 3d model domain. We give comprehensive experiments on two public datasets, and the results demonstrate the superiority of the proposed method. Especially on MI3dOR-2 dataset, our method outperforms the current state-of-the-art methods with gains of 2.88% for the strictest retrieval metric NN.

关键词： 3d model retrieval Unsupervised domain adaptation Feature disentangle learning

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3d model retrieval Using Constructive Solid Geometry in Virtual Reality 3

3D Model Retrieval Using Constructive Solid Geometry in Virt...

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3rd IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Boerlin, Samuel Gasser, Ralph Spiess, Florian Schuldt, Heiko Univ Basel Basel Switzerland

ISBN: (纸本)9781728174631

3d models play an increasingly important role in various areas, ranging from engineering to the cultural heritage domain. Therefore, tools to effectively and efficiently manage, explore and search large 3d model collections have become more important over the years. Most solutions so far use conventional 2d user interfaces and interaction relies on mouse and keyboard input. In this paper, we present vitrivr-VR, a 3d model retrieval system featuring a virtual reality (VR) user interface based on the multimedia search system vitrivr. Query formulation and results presentation takes place in a VR enviroment, in which users immerse themselves. vitrivr-VR enables the sculpting of 3d models through constructive solid geometry (CSG) using a VR controller and the use of these sculpted objects as query objects. To the best of our knowledge, vitrivr-VR is the first system that combines CSG and VR to enable 3d model retrieval.

关键词： 3d model retrieval virtual reality constructive solid geometry multimedia retrieval open source

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Gradual adaption with memory mechanism for image-based 3d model retrieval

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IMAGE ANd VISION COMPUTING 2022年 123卷

作者： Song, dan Ling, Yuting Li, Tianbao Zhang, Ting Jin, Guoqing Guo, Junbo Li, Xuanya Peoples Daily Online State Key Lab Commun Content Cognit Beijing 100733 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Baidu Inc Beijing 100105 Peoples R China

With the development of 3d modeling technology and its wide application in different fields, the number of 3d models increases rapidly, making 3d model retrieval a hot topic in current research. Compared with other 3d model retrieval methods, 2d image-based unsupervised 3d model retrieval takes the 2d images which have rich labels and are easy to obtain as the queries, and also takes into account the difficulties of labeling 3d models. 2d image-based unsupervised 3d model retrieval is a retrieval task involving cross-domain adaptation problem, which main challenge is the excessive domain gap. In this paper, we propose a cross-domain 3d model retrieval method of memory mechanism based on disentangled feature learning. The disentangled feature learning enables to disentangle the twisted original features into the isolated domain-invariant features and domain specific features, where the former is to be aligned to narrow the domain gap. On this basis, the memory mechanism selects feature vectors from class memory modules constructed by class representative features of the opposite domain for every sample, which are used to update the domain-invariant features with gradient weight. The memory mechanism can gradually improve the adaptability of the model to the very different two domains. Experiments are conducted on the public datasets MI3dOR and MI3dOR-2 to verify the feasibility and the superiority of the proposed method. Especially on MI3dOR-2 dataset, our method outperforms the current state-ofthe-art methods with gains of 7.71% for the strictest retrieval metric NN.(c) 2022 Elsevier B.V. All rights reserved.

关键词： 3d model retrieval Unsupervised learning domain adaptation

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A Novel Patch Convolutional Neural Network for View-based 3d model retrieval 21

A Novel Patch Convolutional Neural Network for View-based 3D...

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29th ACM International Conference on Multimedia (MM)

作者： Gao, Zan Shao, Yuxiang Guan, Weili Liu, Meng Cheng, Zhiyong Chen, Shengyong Qilu Univ Technol Shandong Artif Intelligence Inst Jinan Peoples R China Monash Univ Sch Informat Technol Melbourne Vic Australia Shandong Jianzhu Univ Jinan Peoples R China Shandong AI Inst Jinan Peoples R China Tianjin Univ Technol Tianjin Peoples R China

ISBN: (纸本)9781450386517

Recently, many view-based 3d model retrieval methods have been proposed and have achieved state-of-the-art performance. Most of these methods focus on extracting more discriminative view-level features and effectively aggregating the multi-view images of a 3d model, but the latent relationship among these multi-view images is not fully explored. Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images. To capture associations among views, in this work, we propose a novel patch convolutional neural network (PCNN) for view-based 3d model retrieval. Specifically, we first employ a CNN to extract patch features of each view image separately. Secondly, a novel neural network module named PatchConv is designed to exploit intrinsic relationships between neighboring patches in the feature space to capture long-range associations among multi-view images. Then, an adaptive weighted view layer is further embedded into PCNN to automatically assign a weight to each view according to the similarity between each view feature and the view-pooling feature. Finally, a discrimination loss function is employed to extract the discriminative 3d model feature, which consists of softmax loss values generated by the fusion classifier and the specific classifier. Extensive experimental results on two public 3d model retrieval benchmarks, namely, the modelNet40, and modelNet10, demonstrate that our proposed PCNN can outperform state-of-the-art approaches, with mAP values of 93.67%, and 96.23%, respectively.

关键词： 3d model retrieval Patch Convolutional Neural Network Adaptive Weighted View Layer discrimination Loss

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3d model retrieval based on deep learning approach with weighted three-view deep features

3D model retrieval based on deep learning approach with weig...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Jiang, Xuemei Li, Yaqi Hu, Jiwei Lam, Kin-Man Wuhan Univ Technol Sch Informat Engn Wuhan Hubei Peoples R China Hubei Key Lab Broadband Wireless Commun & Sensor Wuhan Hubei Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781510638365

With the development of computer graphics and three-dimensional (3d) modeling technology, 3d model retrieval has been widely used in different applications, such as industrial design, virtual reality, medical diagnosis, etc. Massive data brings new opportunities and challenges to the development of the 3d model retrieval technology. However, with the emergence of complex models, traditional retrieval algorithms are not applicable to some extent. One important reason for this is that the traditional content-based retrieval methods do not take the spatial information of 3d models into account during feature extraction. Therefore, how to use the spatial information of a 3d model to obtain a more extensive feature has become a significant issue. In our proposed algorithm, we first normalize and voxelize the model, and then extract features from different views of the voxelized model. Secondly, deep features are extracted by using our proposed feature learning network. Then, a new feature weighting algorithm is applied to our 3d-view-based features, which can emphasize the more important views of the 3d models, so the retrieval performance can be improved. The experimental results on the standard 3d model dataset, Princeton modelNet10, show that our model can achieve promising performance.

关键词： 3d model retrieval voxelization deep features feature weighting deep learning

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Exploring deep Learning for View-Based 3d model retrieval

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ACM TRANSACTIONS ON MULTIMEdIA COMPUTING COMMUNICATIONS ANd APPLICATIONS 2020年第1期16卷 1-21页

作者： Gao, Zan Li, Yinming Wan, Shaohua Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Key Lab Comp Vis & Syst Minist Educ Tianjin 300384 Peoples R China Qilu Univ Technol Shandong Artificial Intelligence Inst Shandong Comp Sci Ctr Natl Supercomp Ctr JinanShandong Acad Sci Jinan 250014 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China

In recent years, view-based 3d model retrieval has become one of the research focuses in the field of computer vision and machine learning. In fact, the 3d model retrieval algorithm consists of feature extraction and similarity measurement, and the robust features play a decisive role in the similarity measurement. Although deep learning has achieved comprehensive success in the field of computer vision, deep learning features are used for 3d model retrieval only in a small number of works. To the best of our knowledge, there is no benchmark to evaluate these deep learning features. To tackle this problem, in this work we systematically evaluate the performance of deep learning features in view-based 3d model retrieval on four popular datasets (ETH, NTU60, PSB, and MVREd) by different kinds of similarity measure methods. In detail, the performance of hand-crafted features and deep learning features are compared, and then the robustness of deep learning features is assessed. Finally, the difference between single-view deep learning features and multi-view deep learning features is also evaluated. By quantitatively analyzing the performances on different datasets, it is clear that these deep learning features can consistently outperform all of the hand-crafted features, and they are also more robust than the hand-crafted features when different degrees of noise are added into the image. The exploration of latent relationships among different views in multi-view deep learning network architectures shows that the performance of multi-view deep learning outperforms that of single-view deep learning features with low computational complexity.

关键词： 3d model retrieval benchmark deep learning features handcrafted feature

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