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Multi-view ensemble manifold regularization for 3d object recognition

INFORMATION SCIENCES 2015年 320卷 395-405页

作者： Hong, Chaoqun Yu, Jun You, Jane Chen, Xuhui Tao, dapeng Xiamen Univ Technol Coll Comp Sci & Informat Engn Xiamen 361024 Fujian Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Zhejiang Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China

View-based methods are popular in 3d object recognition. However, current methods with traditional classifiers are usually based on one-to-one view matching and fail to capture the structure information of multiple views. Some multi-view based methods take different views into consideration, but they still treat views separately. In this paper, we propose a novel 3d object recognizing method based on multi-view data fusion, called Multi-view Ensemble Manifold Regularization (MEMR). In this method, we model image features with a regularization term for SVM. To train this modified SVM, multi-view learning is achieved with alternating optimization. Hypergraph construction is used to better capture the connectivity among views. Experimental results show that the accuracy rate has been improved by 20-25%, which demonstrates the effectiveness of the proposed method. (C) 2015 Elsevier Inc. All rights reserved.

关键词： 3d object recognition Support vector machine Manifold learning Multi-view fusion Hypergraph

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HCCG: Efficient high compatibility correspondence grouping for 3d object recognition and 6d pose estimation in cluttered scenes

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MEASUREMENT 2022年 197卷

作者： Wu, Lang Li, Xi Zhong, Kai Li, Zhongwei Wang, Congjun Shi, Yusheng Huazhong Univ Sci & Technol Sch Mat Sci & Engn State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China Res Inst Huazhong Univ Sci & Technol Shenzhen Shenzhen 518063 Peoples R China

Recognizing and localizing queried objects in point clouds is a critical technique for robotic manipulation and bin picking tasks. Even though it has been steadily studied, it is still a challenging task for scenes with heavy occlusion and clutter. To copy with this intractable problem, this paper presents a fast and robust 3d object recognition framework, especially an efficient high compatibility correspondence grouping (HCCG) technique achieved by the correspondence ranking, clustering and expanding operations. Utilizing the HCCG technique, we first cluster the compatible correspondences into several high compatibility correspondence groups. Then, for each group, a 6doF pose hypothesis is generated by using a point pair feature constraints (PPFCs)-based outlier removal module and a local reference frame (LRF)-based pose estimation algorithm. Finally, a robust pose verification operator is carried out to reject the false positive pose hypotheses and pick up the correct target object pose. Our approach is not only suitable for the multi-object recognition task, but also inherently yields the capability of detecting multiple instances thanks to the efficient HCCG technique. Extensive experiments on four challenging datasets show that our approach yields efficient and timely solutions and its advantages are further verified by comparing with the latest methods.

关键词： 3d object recognition Correspondence grouping 6doF pose hypothesis Point pair feature constraints Local reference frame

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View-relation constrained global representation learning for multi-view-based 3d object recognition

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APPLIEd INTELLIGENCE 2023年第7期53卷 7741-7750页

作者： Xu, Ruchang Mi, Qing Ma, Wei Zha, Hongbin Beijing Univ Technol Fac Informat Technol Beijing 100020 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept MOE Beijing 100871 Peoples R China

Multi-view observations provide complementary clues for 3d object recognition, but also include redundant information that appears different across views due to view-dependent projection, light reflection and self-occlusions. This paper presents a view-relation constrained global representation network (VCGR-Net) for 3d object recognition that can mitigate the view interference problem at all phases, from view-level source feature generation to multi-view feature aggregation. Specifically, we determine inter-view relations via LSTM implicitly. Based on the relations, we construct a two-stage feature selection module to filter features at each view according to their importance to the global representation and their reliability as observations at specific views. The selected features are then aggregated by referring to intra- and inter-view spatial context to generate global representation for 3d object recognition. Experiments on the ModelNet40 and ModelNet10 datasets demonstrate that the proposed method can suppress view interference and therefore outperform state-of-the-art methods in 3d object recognition.

关键词： 3d object recognition Multi-views View-relation constraints 3d global representation

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Efficient 3d object recognition in mobile edge environment

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JOURNAL OF CLOUd COMPUTING-AdVANCES SYSTEMS ANd APPLICATIONS 2022年第1期11卷 1-16页

作者： Song, Mofei Guo, Qi Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Peoples R China

3d object recognition has great research and application value in the fields of automatic drive, virtual reality, and commercial manufacturing. Although various deep models have been exploited and achieved remarkable results for 3d object recognition, their computational cost is too high for most mobile applications. This paper combines edge computing and 3d object recognition into a powerful and efficient framework. It consists of a cloud-based rendering stage and a terminal-based recognition stage. In the first stage, inspired by the cloud-based rendering technique, we upload the 3d object data from the mobile device to the edge cloud server for multi-view rendering. The rendering stage utilizes the powerful computing resource in the edge cloud server to generate multiple view images of the given 3d object from different views by parallel high-quality rendering. during the terminal-based recognition stage, we integrate a lightweight CNN architecture and a neural network quantization technique into a 3d object recognition model based on the multiple images rendered in the edge cloud server, which can be executed fast in the mobile device. To reduce the cost of network training, we propose a novel semi-supervised 3d deep learning method with fewer labeled samples. Experiments demonstrate that our method achieves competitive performance compared to the state-of-the-art methods with low latency running in the mobile edge environment.

关键词： Cloud computing deep learning 3d object recognition

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Combined model-based 3d object recognition

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INTERNATIONAL JOURNAL OF PATTERN recognition ANd ARTIFICIAL INTELLIGENCE 2005年第7期19卷 839-852页

作者： Kim, S Jang, G Lee, WH Kweon, IS Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon South Korea

This paper presents a combined model-based 3d object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3d object identification and pose estimation.

关键词： combined object model robust properties of HVS bottom-up top-down 3d object recognition pose estimation

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CoSPAIR: Colored Histograms of Spatial Concentric Surflet-Pairs for 3d object recognition

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ROBOTICS ANd AUTONOMOUS SYSTEMS 2016年 75卷 558-570页

作者： Logoglu, K. Berker Kalkan, Sinan Temizel, Alptekin Middle E Tech Univ Inst Informat TR-06531 Ankara Turkey Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Introduction of RGB-d sensors together with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this paper, two spatially enhanced local 3d descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surflet-Pairs (SPAIR) and Colored SPAIR (CoSPAIR). The proposed descriptors are compared against the state-of-the-art local 3d descriptors that are available in Point Cloud Library (PCL) and their object recognition performances are evaluated on several publicly available datasets. The experiments demonstrate that the proposed CoSPAIR descriptor outperforms the state-of-the-art descriptors in both category-level and instance-level recognition tasks. The performance gains are observed to be up to 9.9 percentage points for category-level recognition and 16.49 percentage points for instance-level recognition over the second-best performing descriptor. (C) 2015 Elsevier B.V. All rights reserved.

关键词： 3d descriptors 3d object recognition Point clouds RGB-d

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A 3d object recognition Method From LidAR Point Cloud Based on USAE-BLS

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15267-15277页

作者： Tian, Yifei Song, Wei Chen, Long Fong, Simon Sung, Yunsick Kwak, Jeonghoon Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China North China Univ Technol Dept Comp Sci & Technol Beijing 100144 Peoples R China Dongguk Univ Dept Multimedia Engn Seoul 04620 South Korea

Environmental perception provides the necessary information for unmanned ground vehicles to recognize and interact with surrounding objects. Velodyne light detection and ranging (LidAR) is widely used for this purpose due to its significant advantages such as high precision and being uninfluenced by varying illuminations. However, the unstructured distribution of LidAR point clouds always affects the performance of feature extraction and object recognition. Moreover, the numbers of parameters in most deep learning models of object recognition are very large and the training process costs lots of computation consumption. This paper proposes a broad learning system (BLS) variant with a unified space autoencoder (USAE) as a lightweight model to recognize 3d objects. When the proposed method was evaluated on the LidAR point cloud dataset and ModelNet10 dataset, the experimental results indicated that the recognition accuracy of our USAE-BLS model was similar to that of state-of-the-art 3d object recognition models. Moreover, the USAE-BLS has a much smaller model size and shorter training time than that of the deep learning models.

关键词： Point cloud compression Three-dimensional displays object recognition Feature extraction Solid modeling Training Laser radar 3d object recognition broad learning system LidAR point cloud unified space autoencoder

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A fast 3d object recognition algorithm using plane-constrained point pair features

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MULTIMEdIA TOOLS ANd APPLICATIONS 2020年第39-40期79卷 29305-29325页

作者： Xiao, Zhengtao Gao, Jian Wu, dongqing Zhang, Lanyu Chen, Xin Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Peoples R China Guangdong Polytech Ind & Commerce Sch Electromech Engn Guangzhou 510510 Peoples R China Zhongkai Univ Agr & Engn Sch Computat Sci Guangzhou 510220 Peoples R China

The point pair feature (PPF) algorithm is one of the best-performing 3d object recognition algorithms. However, the high dimensionality of its search space is a disadvantage of this algorithm. This high dimensionality means the feature matching process contains a large number of uninformative features, which reduces recognition speed. To solve this problem and improve the object recognition speed, this paper proposes a fast 3d object recognition algorithm based on the plane-constrained point pair features. By utilizing the property of the coplanar point pair features and the characteristics of the object placement plane, the proposed algorithm extracts the object placement plane through convex hull area calculation, eliminates irrelevant point pair features, and then performs object recognition with the reduced point pair feature descriptors for the feature matching. Experimental results demonstrate that the proposed algorithm significantly reduces the number of feature descriptors and accelerates the recognition speed of 3d objects in a complex background. Compared to the original point pair feature algorithm, the proposed method can achieve better performance and efficiency for 3d object recognition.

关键词： 3d object recognition Point pair features Feature descriptor Feature matching Convex hull

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Learning high-level features by fusing multi-view representation of MLS point clouds for 3d object recognition in road environments

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ISPRS JOURNAL OF PHOTOGRAMMETRY ANd REMOTE SENSING 2019年 150卷 44-58页

作者： Luo, Zhipeng Li, Jonathan Xiao, Zhenlong Mou, Z. Geroge Cai, Xiaojie Wang, Cheng Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart Cities 422 Siming Rd South Xiamen 361005 FJ Peoples R China Univ Waterloo Dept Geog 200 Univ Ave West Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Environm Management & Syst Design Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Most existing 3d object recognition methods still suffer from low descriptiveness and weak robustness although remarkable progress has made in 3d computer vision. The major challenge lies in effectively mining high-level 3d shape features. This paper presents a high-level feature learning framework for 3d object recognition through fusing multiple 2d representations of point clouds. The framework has two key components: (1) three discriminative low-level 3d shape descriptors for obtaining multi-view 2d representation of 3d point clouds. These descriptors preserve both local and global spatial relationships of points from different perspectives and build a bridge between 3d point clouds and 2d Convolutional Neural Networks (CNN). (2) A two-stage fusion network, which consists of a deep feature learning module and two fusion modules, for extracting and fusing high-level features. The proposed method was tested on three datasets, one of which is Sydney Urban objects dataset and the other two were acquired by a mobile laser scanning (MLS) system along urban roads. The results obtained from comprehensive experiments demonstrated that our method is superior to the state-of-the-art methods in descriptiveness, robustness and efficiency. Our method achieves high recognition rates of 94.6%, 93.1% and 74.9% on the above three datasets, respectively.

关键词： Convolutional neural networks 3d object recognition MLS point clouds Multi-view representation Two-stage fusion network

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2004年第1期57卷 5-22页

作者： Cyr, CM Kimia, BB Brown Univ Div Engn Lab Engn Man Machine Syst Providence RI 02912 USA

This paper describes a view-based method for recognizing 3d objects from 2d images. We employ an aspect-graph structure, where the aspects are not based on the singularities of visual. mapping but are instead formed using a notion of shape similarity between views. Specifically, the viewing sphere is endowed with a metric of dis-similarity for each pair of views and the problem of aspect generation is viewed as a "segmentation" of the viewing sphere into homogeneous regions. The viewing sphere is sampled at regular (5 degree) intervals and the similarity metric is used in an iterative procedure to combine views into aspects with a prototype representing each aspect. This is done in a "region-growing" regime which stands in contrast to the usual "edge detection" styles to computing the aspect graph. The aspect growth is constrained such that two aspects of an object remain distinct under the given similarity metric. Once the database of 3d objects is organized as a set of aspects, and prototypes for these aspects for each object, unknown views of database objects are compared with the prototypes and the results are ordered by similarity. We use two similarity metrics for shape, one based on curve matching and the other based on matching shock graphs, which for a database of 64 objects and unknown views of objects from the database give a recall rate of (90.3%, 74.2%, 59.7%) and (95.2%, 69.0%, 57.5%), respectively, for the top three matches;cumulative recall rate based on the top three matches is 98% and 100%, respectively. The result of indexing unknown views of objects not in the database also produce intuitive matches. We also develop a hierarchical indexing scheme to prune unlikely objects at an early stage to improve the efficiency of indexing, resulting in savings of 35% at the top level and of 55% at the next level, cumulatively.

关键词： 3d object recognition aspect-graph view-based recognition shape similarity characteristic views

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