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Search Results (14)
  • Open Access

    ARTICLE

    A Random Fusion of Mix3D and PolarMix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

    Bo Liu1,2, Li Feng1,*, Yufeng Chen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 845-862, 2024, DOI:10.32604/cmes.2024.047695

    Abstract This paper focuses on the effective utilization of data augmentation techniques for 3D lidar point clouds to enhance the performance of neural network models. These point clouds, which represent spatial information through a collection of 3D coordinates, have found wide-ranging applications. Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities. Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds. However, there has been a lack of focus on making the… More >

  • Open Access

    ARTICLE

    Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

    Shoukun Xu1, Lujun Zhang1, Guangqi Jiang1, Yining Hua2, Yi Liu1,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853

    Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

  • Open Access

    ARTICLE

    CFSA-Net: Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

    Jun Shu1,2, Shuai Wang1,2, Shiqi Yu1,2, Jie Zhang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

    Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for… More >

  • Open Access

    ARTICLE

    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz1, Ki-Chul Kwon1, F. M. Fahmid Hossain1, Md. Biddut Hossain1, Rupali Kiran Shinde1, Sang-Keun Gil2, Nam Kim1,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205

    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field… More >

  • Open Access

    ARTICLE

    Point Cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

    Sidong Wu, Cuiping Duan, Bufan Ren, Liuquan Ren, Tao Jiang, Jianying Yuan*, Jiajia Liu, Dequan Guo

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2707-2726, 2023, DOI:10.32604/iasc.2023.038948

    Abstract The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point cloud. In this paper, to address the semantic segmentation for safety driving of unmanned shuttle buses, an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios (such as campus). Firstly, we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation, and then propose an improved semantic segmentation model based on Cylinder3D,… More >

  • Open Access

    ARTICLE

    Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics

    Xiaozhong Chen*

    Molecular & Cellular Biomechanics, Vol.20, No.1, pp. 1-14, 2023, DOI:10.32604/mcb.2022.026964

    Abstract Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface, the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are More >

  • Open Access

    ARTICLE

    Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

    Yan Zhu1,2, Lili Tian2, Fan Ye2, Gaofeng Sun1, Xianyong Fang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1835-1856, 2023, DOI:10.32604/cmes.2023.025662

    Abstract Non-rigid registration of point clouds is still far from stable, especially for the largely deformed one. Sparse initial correspondences are often adopted to facilitate the process. However, there are few studies on how to build them automatically. Therefore, in this paper, we propose a robust method to compute such priors automatically, where a global and local combined strategy is adopted. These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences. To further utilize the matches, this paper also proposes a novel registration method based More >

  • Open Access

    ARTICLE

    3D Object Detection with Attention: Shell-Based Modeling

    Xiaorui Zhang1,2,3,4,*, Ziquan Zhao1, Wei Sun4,5, Qi Cui6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230

    Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate… More >

  • Open Access

    ARTICLE

    Aggregate Point Cloud Geometric Features for Processing

    Yinghao Li1,2,3, Renbo Xia1,2,*, Jibin Zhao1,2,*, Yueling Chen1,2, Liming Tao1,2,3, Hangbo Zou1,2,3, Tao Zhang1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 555-571, 2023, DOI:10.32604/cmes.2023.024470

    Abstract As 3D acquisition technology develops and 3D sensors become increasingly affordable, large quantities of 3D point cloud data are emerging. How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved. The point cloud geometric information is hidden in disordered, unstructured points, making point cloud analysis a very challenging problem. To address this problem, we propose a novel network framework, called Tree Graph Network (TGNet), which can sample, group, and aggregate local geometric features. Specifically, we construct a Tree Graph by explicit rules, which consists More >

  • Open Access

    REVIEW

    Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235

    Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific… More >

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