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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Asadi-Aghbolaghi, Maryam Ramezanpour, Sadegh Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479944095

A novel approach for recognizing human actions using sequences of 3d point clouds of agents over time is presented. It is claimed that some regions that have a long distance to the body center (boundary regions of human body) are very discriminative for understanding human actions. Based on this idea, a novel descriptor based on weighted boundary of 3d point cloud is introduced to recognize the actions. Unlike previously published methods, this descriptor is invariant to scale, translation, and rotation. A dynamic time alignment technique is used as a similarity measure for classification. Experimental results on i3dPost dataset demonstrate the effectiveness of proposed method compared to other existing methods.

关键词： human action recognition 3d feature descriptor point cloud

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Evaluation of 3d feature descriptors for Multi-modal data Registration

Evaluation of 3D Feature Descriptors for Multi-modal Data Re...

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International Conference on 3d Vision (3dV)

作者： Kim, Hansung Hilton, Adrian Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7NT Surrey England

ISBN: (纸本)9780769550671

We propose a framework for 2d/3d multi-modal data registration and evaluate 3d feature descriptors for registration of 3d datasets from different sources. 3d datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2d images are converted into 3d structure by stereo or multi-view reconstruction techniques and registered to a unified 3d domain with other datasets in a 3d world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3d feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3d feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration.

关键词： 3d feature descriptor Multi-modal data registration 2d/3d registration Evaluation

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SigVox - A 3d feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

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ISPRS JOURNAL OF PHOTOGRAMMETRY ANd REMOTE SENSING 2017年 128卷 111-129页

作者： Wang, Jinhu Lindenbergh, Roderik Menenti, Massimo Delft Univ Technol Dept Geosci & Remote Sensing Bldg 23Stevinweg 1Post Box 5048 NL-2628CN Delft Netherlands Chinese Acad Sci Acad Optoelect Key Lab Quantitat Remote Sensing Informat Technol 9 Dengzhuang South Rd Beijing 100094 Peoples R China

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3d multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3d significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3d SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3d SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the similar to 170 automatically recognized objects is approximately 95%. The results demo

关键词： Mobile laser scanning Point cloud Octree Multiple scale feature Object recognition 3d feature descriptor

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Scale invariant point feature (SIPF) for 3d point clouds and 3d multi-scale object detection

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NEURAL COMPUTING & APPLICATIONS 2018年第5期29卷 1209-1224页

作者： Lin, Baowei Wang, Fasheng Zhao, Fangda Sun, Yi Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Dalian Neusoft Univ Informat Dept Elect Engn Dalian Peoples R China Hiroshima Univ Grad Sch Engn Dept Informat Engn Higashihiroshima Japan

3d point clouds are important for the reconstruction of environment. However, comparing to the artificial VR scene representation methods, 3d point clouds are more difficult to correspond to real scenes. In this paper, a method for detecting keypoints and describing scale invariant point feature of 3d point clouds is proposed. To detect, we first select keypoints as the saliency points with fast changing speed along with all principal directions of the searching area of the point cloud. The searching area is a searching keyscale which represents the unique scale size of the point cloud. Then, the descriptor is encoded based on the shape of a border or silhouette of an object to be detected or recognized. We also introduce a vote-casting-based 3d multi-scale object detection method. Experimental results based on synthetic data, real data and vote-casting scheme show that we can easily deal with the different tasks without additional information.

关键词： VR Point cloud Keypoints detector 3d feature descriptor 3d object detection

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R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 2710-2725页

作者： Kadam, Pranav Zhang, Min Liu, Shan Kuo, C-C Jay Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA Tencent Amer Tencent Media Lab Palo Alto CA 94306 USA

Inspired by the recent PointHop classification method, an unsupervised 3d point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines a local reference frame (LRF) for every point using its nearest neighbors and finds local attributes. Next, R-PointHop obtains local-to-global hierarchical features by point downsampling, neighborhood expansion, attribute construction and dimensionality reduction steps. Thus, point correspondences are built in hierarchical feature space using the nearest neighbor rule. Afterwards, a subset of salient points with good correspondence is selected to estimate the 3d transformation. The use of the LRF allows for invariance of the hierarchical features of points with respect to rotation and translation, thus making R-PointHop more robust at building point correspondence, even when the rotation angles are large. Experiments are conducted on the 3dMatch, ModelNet40, and Stanford Bunny datasets, which demonstrate the effectiveness of R-PointHop for 3d point cloud registration. R-PointHop's model size and training time are an order of magnitude smaller than those of deep learning methods, and its registration errors are smaller, making it a green and accurate solution. Our codes are available on GitHub (https://***/pranavkdm/R-PointHop).

关键词： Point cloud compression Three-dimensional displays feature extraction Task analysis Transforms deep learning Principal component analysis Point cloud registration rotation invariance local reference frame (LRF) 3d feature descriptor

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3d object recognition using scale-invariant features

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VISUAL COMPUTER 2019年第1期35卷 71-84页

作者： Lim, Jeonghun Lee, Kunwoo Seoul Natl Univ Inst Engn Res Sch Mech Engn Seoul South Korea

As 3d scanning technology develops, it becomes easier to acquire various 3d surface data;thus, there is a growing need for 3d data registration and recognition technology. Many existing studies use local descriptors using local surface patches, and most of them use a fixed support radius, so they cannot cope perfectly when the model and scene have different scales. In this study, we propose a perfectly scale-invariant feature selection algorithm by extending the 2d SIFT algorithm (Lowe in Int J Comput Vis 60(2):91-110, 2004) to a 3d mesh. The feature selection method proposed in this study can obtain highly repeatable feature points and support radii regardless of mesh scale. The selected features can effectively describe the local information by the new shape descriptor proposed in this study. Unlike existing shape descriptors, it is possible to perform scale-invariant 3d object recognition and achieve a high recognition rate when combined with the feature point selection algorithm proposed in this study by using the gradients of the scalar functions defined on the 3d surface. We also reduced the searching space and lowered the false positive rate by suggesting a new RANSAC-based transformation hypotheses generation algorithm. Our 3d object recognition algorithm achieves recognition rates of 100 and 98.5%, respectively, when tested on the U3OR and CFVd datasets, exceeding the results of previous studies.

关键词： 3d feature selection 3d feature descriptor 3d object recognition Scale-invariant recognition

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Trigonometric projection statistics histograms for 3d local feature representation and shape description

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PATTERN RECOGNITION 2023年第1期143卷

作者： Liu, Xingsheng Li, Anhu Sun, Jianfeng Lu, Zhiyong Tongji Univ Sch Mech Engn Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China

feature representation as a significant approach to three-dimensional (3d) shape description has been widely employed in computer vision. However, most existing methods are suffering from the emerging challenges for descriptiveness, robustness and efficiency. This paper presents a novel feature descriptor named trigonometric projection statistics histograms (TPSH). By constructing the repeatable local refer-ence frame based on a multi-attribute weighting strategy, TPSH can address many prevailing nuisances such as noise, occlusion and varying resolution. The trigonometric projection mechanism is originally proposed for TPSH generation, which combines two perspective views to encode both spatial distribution and geometrical measurements from local shape into statistics histograms. The experimental evaluation on public datasets proves that TPSH outperforms state-of-the-art methods in descriptiveness and robust-ness while maintaining storage compactness and computational efficiency. It is demonstrated that TPSH can not only be suited for 3d object recognition and shape registration, but also generalized across vari-ous acquisition devices, data modalities and application scenarios. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： 3d feature descriptor Local reference frame Trigonometric projection mechanism Object recognition Shape registration

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Kdd: A kernel density based descriptor for 3d point clouds

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PATTERN RECOGNITION 2021年 111卷 107691-107691页

作者： Zhang, Yuhe Li, Chunhui Guo, Bao Guo, Chenhao Zhang, Shunli Northwest Univ Sch Informat Sci & Technol Xian Peoples R China

3d feature description is one of the central techniques that rely on point clouds since a lot of point cloud processing techniques apply the point-to-point correspondences that are achieved via feature descriptors as input data. The feature descriptor encodes the information of the underlying surface around the feature point so as to make a local surface distinguished from another. The focus of the existing descriptors is accumulating the geometric or topological measurements into histograms or encoding the 2d images that are acquired by rotationally projecting the 3d local surfaces onto 2d planes. Histograms can hardly deal with three or more dimensional information, and the rotational projection operation does bring much unnecessary intermediate computations. To overcome these limitations, in this article, a descriptor named Kernel density descriptor (Kdd) has been presented. One core contribution of this method is to encode the information of the whole 3d space around the feature point via kernel density estimation, and another is providing the strategy for selecting different matching metrics for datasets with diverse levels of resolution qualities. We compare Kdd against several representative descriptors on publicly available datasets, the experimental results demonstrate that the Kdd descriptor achieves a satisfactory and balanced performance in terms of descriptiveness, robustness, and compactness, furthermore, the comparisons validate the overall superiority of our method. The benefits and applicability on object registration and recognition and 3d object reconstruction are demonstrated by the favorable results that are obtained for both public datastes and the real-world point clouds of Terracotta fragments. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： 3d feature descriptor Kernel density estimation Point cloud registration KL divergence

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HYBRId 3d feature dESCRIPTION ANd MATCHING FOR MULTI-MOdAL dATA REGISTRATION

HYBRID 3D FEATURE DESCRIPTION AND MATCHING FOR MULTI-MODAL D...

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IEEE International Conference on Image Processing

作者： Hansung Kim Adrian Hilton Centre for Vision Speech and Signal Processing University of Surrey

ISBN: (纸本)9781479957521

We propose a robust 3d feature description and registration method for 3d models reconstructed from various sensor devices. General 3d feature detectors and descriptors generally show low distinctiveness and repeatability for matching between different data modalities due to differences in noise and errors in geometry. The proposed method considers not only local 3d points but also neighbouring 3d keypoints to improve keypoint matching. The proposed method is tested on various multi-modal datasets including LIdAR scans, multiple photos, spherical images and RGBd videos to evaluate the performance against existing methods.

关键词： 3d feature descriptor Multi-modal data registration 2d/3d registration

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View Invariant Human Action Recognition Using Fourier-based and Radon-based Point Cloud Analysis 7

View Invariant Human Action Recognition Using Fourier-based ...

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7th International Symposium on Telecommunications (IST)

作者： Asadi-Aghbolaghi, Maryam Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479953592

Human action recognition has been one of the most challenging topics in computer vision during the last decade. This paper presents a novel approach for recognizing view independent human actions based on analysis of Fourier transform and Radon transform of self similarity matrix of features obtained from the action. The proposed feature descriptor is extracted from human point cloud over the time and is based on the key idea that some parts of human body which have a longer distance from the body center are more discriminative for human action recognition purposes. The effectiveness of the proposed method is demonstrated with the experiments on i3dPost dataset.

关键词： human action recognition point cloud 3d feature descriptor Fourier transform Radon transfrom

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