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  • Open Access

    ARTICLE

    MarkNeRF: Watermarking for Neural Radiance Field

    Lifeng Chen1,2, Jia Liu1,2,*, Wenquan Sun1,2, Weina Dong1,2, Xiaozhong Pan1,2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.051608

    Abstract This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field (NeRF) models. We employ an embedding network to integrate the watermark into the images within the training set. Then, the NeRF model is utilized for 3D modeling. For copyright verification, a secret image is generated by inputting a confidential viewpoint into NeRF. On this basis, design an extraction network to extract embedded watermark images from confidential viewpoints. In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario, the verifier can extract More >

  • Open Access

    ARTICLE

    A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

    Jian Luo1,*, Bo Xu1, Tardi Tjahjadi2, Jian Yi1

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018

    Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >

  • Open Access

    ARTICLE

    A Tabletop Nano-CT Image Noise Reduction Network Based on 3-Dimensional Axial Attention Mechanism

    Huijuan Fu, Linlin Zhu, Chunhui Wang, Xiaoqi Xi, Yu Han, Lei Li, Yanmin Sun, Bin Yan*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1711-1725, 2024, DOI:10.32604/cmc.2024.049623

    Abstract Nano-computed tomography (Nano-CT) is an emerging, high-resolution imaging technique. However, due to their low-light properties, tabletop Nano-CT has to be scanned under long exposure conditions, which the scanning process is time-consuming. For 3D reconstruction data, this paper proposed a lightweight 3D noise reduction method for desktop-level Nano-CT called AAD-ResNet (Axial Attention DeNoise ResNet). The network is framed by the U-net structure. The encoder and decoder are incorporated with the proposed 3D axial attention mechanism and residual dense block. Each layer of the residual dense block can directly access the features of the previous layer, which More >

  • Open Access

    ARTICLE

    SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

    Suyi Liu1,*, Jianning Chi1, Chengdong Wu1, Fang Xu2,3,4, Xiaosheng Yu1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450

    Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

  • Open Access

    ARTICLE

    Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

    Do Nang Toan1,*, Lam Thanh Hien2, Ha Manh Toan1, Nguyen Trong Vinh2, Pham Trung Hieu1

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925

    Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

  • Open Access

    ARTICLE

    Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

    Chanho Park1, Takefumi Ogawa2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.048901

    Abstract Six degrees of freedom (6DoF) input interfaces are essential for manipulating virtual objects through translation or rotation in three-dimensional (3D) space. A traditional outside-in tracking controller requires the installation of expensive hardware in advance. While inside-out tracking controllers have been proposed, they often suffer from limitations such as interaction limited to the tracking range of the sensor (e.g., a sensor on the head-mounted display (HMD)) or the need for pose value modification to function as an input interface (e.g., a sensor on the controller). This study investigates 6DoF pose estimation methods without restricting the tracking… More >

  • Open Access

    ARTICLE

    HgaNets: Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

    Wuyan Liang1, Xiaolong Xu2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861

    Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More… More >

  • Open Access

    ARTICLE

    Analysis of Color Landscape Characteristics in “Beautiful Village” of China Based on 3D Real Scene Models

    Yiyi Cen1,3, Wenzheng Jia2, Wen Dai3,*, Chun Wang4, He Wu1

    Revue Internationale de Géomatique, Vol.33, pp. 93-109, 2024, DOI:10.32604/rig.2024.050273

    Abstract Color, as a significant element of village landscapes, serves various functions such as enhancing aesthetic appeal and attractiveness, conveying emotions and cultural values. To explore the three-dimensional spatial characteristics of color landscapes in beautiful villages, this study conducted a comparative experiment involving eight provincial-level beautiful villages and eight ordinary villages in Jinzhai County. Landscape pattern indices were used to analyze the color landscape patterns on the facades of these villages, complemented by a quantitative analysis of color attributes using the Munsell color system. The results indicate that (1) Natural landscape colors in beautiful villages are… More >

  • 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

    Acrylic Finished Leather Upgraded with Thermoplastic Polyurethane Filament using 3D Printing – A New Generation Hybrid Leather of Synthetic and Natural Polymer

    SIVARAJ SUDHAHARa,f, UMAMAHESWARI Gb, JAYA PRAKASH ALLAc, RAGHAVA RAO JONNALAGADDAd, SUGUNA LAKSHMIe, SANJEEV GUPTAf,*

    Journal of Polymer Materials, Vol.40, No.1-2, pp. 33-45, 2023, DOI:10.32381/JPM.2023.40.1-2.3

    Abstract Leather manufacturing process involves a lot of waste disposal which pollutes environment, some of the processes are inevitable. In the present investigation, 3D printing technology was used to reduce the wastage and to cover defective regions in leather. The present study focuses on synthesis of acrylic binder using emulsion polymerization technique. These binders were analysed for solid content for better optimisation of the amount of binder to be utilised for finishing operation. The experimental binder was prepared with 26% solids. Particle size and thermogravimetric analyses were carried out to understand the size and shape of… More >

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