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3d object classification using salient point patterns with application to craniofacial research

PATTERN RECOGNITION 2010年第4期43卷 1502-1517页

作者： Atmosukarto, Indriyati Wilamowska, Katarzyna Heike, Carrie Shapiro, Linda G. Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Seattle Childrens Hosp Craniofacial Ctr Seattle WA USA

This paper presents a new 3d shape representation and classification methodology developed for use in craniofacial dysmorphology studies. The methodology computes low-level features at each point of a 3d mesh representation, aggregates the features into histograms over mesh neighborhoods, learns the characteristics of salient point histograms for each particular application, and represents the points in a 2d spatial map based on a longitude-latitude transformation. Experimental results on the medical classification tasks show that our methodology achieves higher classification accuracy compared to medical experts and existing state-of-the-art 3d descriptors. Additional experimental results highlight the strength and advantage of the flexible framework that allows the methodology to generalize from specific medical classification tasks to general 3d object classification tasks. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： 3d object classification Shape matching Salient points Craniofacial dysmorphology

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3d object classification and Parameter Estimation based on Parametric Procedural Models 26

3D Object Classification and Parameter Estimation based on P...

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26th International Conference on Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)

作者： Getto, Roman Fina, Kenten Jarms, Lennart Kuijper, Arjan Fellner, dieter W. Tech Univ Darmstadt Fraunhoferstr 5 D-64283 Darmstadt Germany Fraunhofer IGD Fraunhoferstr 5 D-64283 Darmstadt Germany

ISBN: (纸本)9788086943404

Classifying and gathering additional information about an unknown 3d objects is dependent on having a large amount of learning data. We propose to use procedural models as data foundation for this task. In our method we (semi-)automatically define parameters for a procedural model constructed with a modeling tool. Then we use the procedural models to classify an object and also automatically estimate the best parameters. We use a standard convolutional neural network and three different object similarity measures to estimate the best parameters at each degree of detail. We evaluate all steps of our approach using several procedural models and show that we can achieve high classification accuracy and meaningful parameters for unknown objects.

关键词： Procedural model parametric model parameterization 3d object classification deep learning

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Inductive Multi-Hypergraph Learning and Its Application on View-Based 3d object classification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第12期27卷 5957-5968页

作者： Zhang, Zizhao Lin, Haojie Zhao, Xibin Ji, Rongrong Gao, Yue Tsinghua Univ Key Lab Informat Syst Secur Minist Educ Beijing Natl Res Ctr Informat Sci & TechnolSch S Beijing 100084 Peoples R China Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Peoples R China

The wide 3d applications have led to increasing amount of 3d object data, and thus effective 3d object classification technique has become an urgent requirement. One important and challenging task for 3d object classification is how to formulate the 3d data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3d objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3d objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3d benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications i

关键词： Inductive learning 3d object classification multi-model multi-hypergraph learning

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CNN-based 3d object classification using Hough space of LidAR point clouds

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HUMAN-CENTRIC COMPUTING ANd INFORMATION SCIENCES 2020年第1期10卷 1-14页

作者： Song, Wei Zhang, Lingfeng Tian, Yifei Fong, Simon Li, Jinming Gozho, Amanda North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macao Peoples R China Beijing Key Lab Urban Intelligent Traff Control T Beijing Peoples R China

With the wide application of Light detection and Ranging (LidAR) in the collection of high-precision environmental point cloud information, three-dimensional (3d) object classification from point clouds has become an important research topic. However, the characteristics of LidAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3d objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3d object classification method using the Hough space of LidAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3d objects. In addition, a semi-automatic 3d object labeling tool is developed to build a LidAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.

关键词： 3d object classification LidAR point clouds Hough space CNN

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L3dOC: Lifelong 3d object classification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 7486-7498页

作者： Liu, Yuyang Cong, Yang Sun, Gan Zhang, Tao dong, Jiahua Liu, Hongsen Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China JD Com Inc Beijing 100176 Peoples R China

3d object classification has been widely applied in both academic and industrial scenarios. However, most state-of-the-art algorithms rely on a fixed object classification task set, which cannot tackle the scenario when a new 3d object classification task is coming. Meanwhile, the existing lifelong learning models can easily destroy the learned tasks performance, due to the unordered, large-scale, and irregular 3d geometry data. To address these challenges, we propose a Lifelong 3d object classification (i.e., L3dOC) model, which can consecutively learn new 3d object classification tasks via imitating "human learning". More specifically, the core idea of our model is to capture and store the cross-task common knowledge of 3d geometry data in a 3d neural network, named as point-knowledge, through employing layer-wise point-knowledge factorization architecture. Afterwards, a task-relevant knowledge distillation mechanism is employed to connect the current task to previous relevant tasks and effectively prevent catastrophic forgetting. It consists of a point-knowledge distillation module and a transforming-space distillation module, which transfers the accumulated point-knowledge from previous tasks and soft-transfers the compact factorized representations of the transforming-space, respectively. To our best knowledge, the proposed L3dOC algorithm is the first attempt to perform deep learning on 3d object classification tasks in a lifelong learning way. Extensive experiments on several point cloud benchmarks illustrate the superiority of our L3dOC model over the state-of-the-art lifelong learning methods.

关键词： 3d object classification lifelong learning point-knowledge task-relevant knowledge distillation

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Adaptive Multi-Hypergraph Convolutional Networks for 3d object classification

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IEEE TRANSACTIONS ON MULTIMEdIA 2023年 25卷 4842-4855页

作者： Nong, Liping Peng, Jie Zhang, Wenhui Lin, Jiming Qiu, Hongbing Wang, Junyi Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Guangxi Normal Univ Coll Phys & Technol Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

3d object classification is an important task in computer vision. In order to explore the high-order and multi-modal correlations among 3d data, we propose an adaptive multi-hypergraph convolutional networks (AMHCN) framework to enhance 3d object classification performance. The proposed network improves the current hypergraph neural networks in two aspects. Firstly, existing networks rely on hyperedge constrained neighborhoods for feature aggregation, which may introduce noise or ignore positive information outside the hyperedges. To this end, we develop the partially absorbing random walks (PARW) to hypergraph for capturing optimal vertex neighborhoods from hypergraph globally. Then, based on the PARW on hypergraph, we design a new hypergraph convolution operator to learn deep embeddings from the optimized high-order correlation, which enables effective information propagation among the most relevant vertices. Secondly, concerning the multi-modal representations in practice, the current multi-modal hypergraph learning models either treat all modalities equally or introduce abundant parameters to learn weights of different modalities. To overcome these shortcomings, we propose a simple but effective dynamic weighting strategy for combining multi-modal representations, in which the importance of each modality can be adjusted adaptively by the loss function. We apply the proposed model to 3d object classification, and the experimental results on two 3d benchmark datasets demonstrate that our method outperforms the state-of-the-art methods, testifying to the effectiveness of both our convolution method and multi-modality fusion strategy.

关键词： Hypergraph learning hypergraph convolution operator partially absorbing random walks multi-modal fusion 3d object classification

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Hierarchical multi-view context modelling for 3d object classification and retrieval

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INFORMATION SCIENCES 2021年 547卷 984-995页

作者： Liu, An-An Zhou, Heyu Nie, Weizhi Liu, Zhenguang Liu, Wu Xie, Hongtao Mao, Zhendong Li, Xuanya Song, dan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Zhejiang Gongshang Univ Hangzhou 310018 Peoples R China AI Res JD Beijing 100105 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Baidu Inc Beijing 100105 Peoples R China

Recent advances in 3d sensors and 3d modelling software have led to big 3d data. 3d object classification and retrieval are becoming important but challenging tasks. One critical problem for them is how to learn the discriminative multi-view visual characteristics. To address it, we proposes a hierarchical multi-view context modelling method (HMVCM). It consists of four key modules. First, the module of view-level context learning is designed to learn visual context features with respect to individual views and their neighbours. This module can imitate the human need to look back and forth to identify and compare the discriminative parts of individual 3d objects based on a joint convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network. Then, a multi-view grouping module is introduced to split views into several groups based on their visual appearance. A raw group-level representation can be obtained by the weighted sum of the view-level descriptors. Furthermore, we employ the Bi-LSTM to exploit the context among adjacent groups to generate group-wise context features. Finally, all group-wise context features are fused into a compact 3d object descriptor according to their significance. Extensive experiments on ModelNet10, ModelNet40 and ShapeNetCore55 demonstrate the superiority of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Multi-view learning 3d object retrieval 3d object classification

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Balanced Class-Incremental 3d object classification and Retrieval

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IEEE TRANSACTIONS ON KNOWLEdGE ANd dATA ENGINEERING 2024年第1期36卷 35-48页

作者： Liu, An-An Lu, Haochun Zhou, Heyu Li, Tianbao Kankanhalli, Mohan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Anhui Peoples R China Natl Univ Singapore Sch Comp Singapore 117543 Singapore

Most existing 3d object classification and retrieval algorithms rely on one-off supervised learning on closed 3d object sets and tend to provide rigid convolutional neural networks with little scalability. Such limitations substantially restrict their potential to learn newly emerged 3d object classes continually in the real world. Aiming to go beyond these limitations, we innovatively propose two new and challenging tasks: class-incremental 3d object classification (CI-3dOC) and class-incremental 3d object retrieval (CI-3dOR), the key to which is class-incremental 3d representation learning. It expects the network to update continually to learn new 3d class representations without forgetting the previously learned ones. To this end, we design a novel balanced distillation network (BdNet) that uses a dual supervision mechanism to balance between consolidating old knowledge (stability) and adapting to new 3d object classes (plasticity) carefully. On the one hand, we employ stability-based supervision to retain the stable and discriminative information of old classes that greatly benefit both classification and retrieval tasks. On the other hand, we use plasticity-based supervision to improve the network's generalization for learning new class 3d representations by transferring knowledge from a temporary teacher network to the current model. By properly handling the relationship between the two modules, we achieve a surprising performance improvement. Furthermore, considering there is no available dataset for evaluation, we build two 3d datasets, INOR-1 and INOR-2, to evaluate these two new tasks. Extensive experimental results demonstrate that our method can significantly outperform other state-of-the-art class-incremental learning methods. Even if we store 500-1000 fewer 3d objects than SOTA methods, BdNet still achieves comparable performance.

关键词： 3d representation learning 3d object classification 3d object retrieval class-incremental learning

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NormalNet: A voxel-based CNN for 3d object classification and retrieval

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NEUROCOMPUTING 2019年 323卷 139-147页

作者： Wang, Cheng Cheng, Ming Sohel, Ferdous Bennamoun, Mohammed Li, Jonathan Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China Murdoch Univ Perth WA Australia Univ Western Australia Perth WA Australia Univ Waterloo Dept Geog & Environm Management Waterloo ON Canada

A common approach to tackle 3d object recognition tasks is to project 3d data to multiple 2d images. Projection only captures the outline of the object, and discards the internal information that may be crucial for the recognition. In this paper, we stay in 3d and concentrate on tapping the potential of 3d representations. We present NormalNet, a voxel-based convolutional neural network (CNN) designed for 3d object recognition. The network uses normal vectors of the object surfaces as input, which demonstrate stronger discrimination capability than binary voxels. We propose a reflection-convolution-concatenation (RCC) module to realize the cony layers, which extracts distinguishable features for 3d vision tasks while reducing the number of parameters significantly. We further improve the performance of NormalNet by combining two networks, which take normal vectors and voxels as input respectively. We carry out a series of experiments that validate the design of the network and achieve competitive performance in 3d object classification and retrieval tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： 3d object classification 3d object retrieval Convolutional neural network Network fusion

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Hypergraph wavelet neural networks for 3d object classification

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NEUROCOMPUTING 2021年 463卷 580-595页

作者： Nong, Liping Wang, Junyi Lin, Jiming Qiu, Hongbing Zheng, Lin Zhang, Wenhui Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Guangxi Normal Univ Coll Phys & Technol Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China

Recently, hypergraph learning has shown great potential in a variety of classification tasks. However, existing hypergraph neural networks lack flexibility in modeling and extracting high-order relationships among data. To solve this problem, we propose a novel framework called hypergraph wavelet neural networks (HGWNN) to explore the high-order correlation in 3d data. Firstly, considering the non-uniformity of most data sets in the real world, we propose a "data-driven" hypergraph construction scheme, which is more efficient than some commonly used hypergraph construction methods. Secondly, in order to efficiently learn deep embeddings from the constructed hypergraph, we propose a hypergraph wavelet convolution operator. It enables efficient information aggregation by fully exploiting the localization property of wavelets. This convolution operator is suitable for both non-uniform and uniform hyper graphs. Finally, we design a new hypergraph regularizer based on the sparse prior of wavelet coefficients to promote local smoothness and avoid network overfitting. We have conducted experiments on object classification tasks on two 3d benchmark datasets: the National Taiwan University (NTU) 3d model data set and the ModelNet40 dataset. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： 3d object classification Hypergraph Hypergraph wavelet transform Hypergraph wavelet convolution Sparse prior

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