Computers & Graphics-Uk最新文献_第10页

An unsupervised video stabilization algorithm based on gyroscope image fusion 基于陀螺仪图像融合的无监督视频稳像算法

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104154

Zhengwei Ren , Mingrui Zou , Lin Bi , Ming Fang

{"title":"An unsupervised video stabilization algorithm based on gyroscope image fusion","authors":"Zhengwei Ren , Mingrui Zou , Lin Bi , Ming Fang","doi":"10.1016/j.cag.2024.104154","DOIUrl":"10.1016/j.cag.2024.104154","url":null,"abstract":"<div><div>Video stabilization aims to enhance the visual quality by reducing jitter and ghosting artifacts caused by camera shaking, yet effectively stabilizing low-quality videos and from complex scenarios remains a significant challenge. While gyroscope-based approaches can address this issue, they struggle with depth variations and translational shaking. In this paper, we propose a coarse-to-fine, unsupervised deep learning video stabilization solution that integrates image and gyroscope data to address these challenges. Our approach excels in stabilizing videos under diverse conditions, managing depth changes, and handling both translational and rotational motion. We utilize gyroscope data to estimate the 3D camera rotation and apply LSTM to predict stable poses. Grid-based motion parameters address depth-related motion, generating a multi-grid warping field that mitigates the significant image jitter caused by camera rotation. Subsequently, we achieve the elimination of residual motion at the pixel level. PDCNet is used to generated confidence maps filter optical flow to minimize disturbances from prominent local areas, while a U-Net architecture smooths the optical flow, performing pixel-level warping to generating finely stabilized frames. Comparative analysis shows that our approach surpasses state-of-the-art methods, particularly in handling complex scenes and achieving stability in challenging conditions.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104154"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust motorcycle graph construction and simplification for semi-structured quad mesh generation 半结构化四网格生成的鲁棒摩托车图构建与简化

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2025.104173

Yanchao Yu , Ni Li , Guanghong Gong

{"title":"Robust motorcycle graph construction and simplification for semi-structured quad mesh generation","authors":"Yanchao Yu , Ni Li , Guanghong Gong","doi":"10.1016/j.cag.2025.104173","DOIUrl":"10.1016/j.cag.2025.104173","url":null,"abstract":"<div><div>Motorcycle graph is widely adopted as an intermediate block in state-of-art semi-structured quad meshing methods. However, constructing and simplifying it on 3D triangle meshes still face challenges in performance and stability. To address these challenges, we present a novel motorcycle graph construction and simplification method for semi-structured quad mesh generation. First, we introduce a piecewise advancing algorithm on parameterized triangle meshes with specially designed data structures to ensure reliable and high-performing motorcycle graph tracing. Second, we enhance the existing zero-collapse procedure with non-intersecting paths creation and feature preserving for T-mesh simplification. Third, we integrate our motorcycle graph construction and simplification algorithm into the state-of-art semi-structured quad meshing pipeline. A comparison with typical state-of-art methods proves that our method can generate quad meshes with superior topological quality and feature preservation capability. We also conduct batch experiments to demonstrate the efficiency, robustness of the proposed method.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104173"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel view synthesis with wide-baseline stereo pairs based on local–global information 基于局部-全局信息的宽基线立体对视图合成方法

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104139

Kai Song, Lei Zhang

{"title":"Novel view synthesis with wide-baseline stereo pairs based on local–global information","authors":"Kai Song, Lei Zhang","doi":"10.1016/j.cag.2024.104139","DOIUrl":"10.1016/j.cag.2024.104139","url":null,"abstract":"<div><div>Novel view synthesis generates images from new views using multiple images of a scene in known views. Using wide-baseline stereo image pairs for novel view synthesis allows scenes to be rendered from varied perspectives with only two images, significantly reducing image acquisition and storage costs and improving 3D scene reconstruction efficiency. However, the large geometry difference and severe occlusion between a pair of wide-baseline stereo images often cause artifacts and holes in the novel view images. To address these issues, we propose a method that integrates both local and global information for synthesizing novel view images from wide-baseline stereo image pairs. Initially, our method aggregates cost volume with local information using Convolutional Neural Network (CNN) and employs Transformer to capture global features. This process optimizes disparity prediction for improving the depth prediction and reconstruction quality of 3D scene representations with wide-baseline stereo image pairs. Subsequently, our method uses CNN to capture local semantic information and Transformer to model long-range contextual dependencies, generating high-quality novel view images. Extensive experiments demonstrate that our method can effectively reduce artifacts and holes, thereby enhancing the synthesis quality of novel views from wide-baseline stereo image pairs.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104139"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Note from the Editor in Chief: Issue 126 总编辑的注释：第126期

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2025.104194

Joaquim Jorge (Editor-in-Chief)

引用次数: 0

A multi-timescale image space model for dynamic cloud illumination 动态云照明的多时间尺度图像空间模型

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104124

Pinar Satilmis , Thomas Bashford-Rogers

引用次数: 0

Exploring user reception of speech-controlled virtual reality environment for voice and public speaking training 探索语音控制的虚拟现实环境对语音和公共演讲培训的用户接受度

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104160

Patryk Bartyzel , Magdalena Igras-Cybulska , Daniela Hekiert , Magdalena Majdak , Grzegorz Łukawski , Thomas Bohné , Sławomir Tadeja

{"title":"Exploring user reception of speech-controlled virtual reality environment for voice and public speaking training","authors":"Patryk Bartyzel , Magdalena Igras-Cybulska , Daniela Hekiert , Magdalena Majdak , Grzegorz Łukawski , Thomas Bohné , Sławomir Tadeja","doi":"10.1016/j.cag.2024.104160","DOIUrl":"10.1016/j.cag.2024.104160","url":null,"abstract":"<div><div>In this paper, we explore the development and assessment of a virtual reality (VR) system designed to enhance public speaking and vocal skills among professional and non-professional speech users alike. The system’s foundation lies in a speech recordings corpus of 529 utterances given during presentations by a total of 15 students. From these data, we extracted voice parameters such as pitch, timbre, and speech rate using speech processing methods. We also asked six expert annotators to evaluate the stress levels present within each presentation. This multi-faceted analysis facilitated the selection of specific parameters for real-time animation control of virtual characters responding dynamically to the change in the speaker’s voice. Through these mechanics, we could cultivate user proficiency in voice modulation, thereby improving overall speaking abilities and confidence. Furthermore, the system fosters self-awareness of vocal quality, promoting proper utilization of the voice in professional settings. Our VR system offers a dual-mode environment that combines traditional public speaking scenarios in front of a virtual audience with a relaxing forest setting, where users can control weather conditions with their voice. To assess the system’s efficacy, we conducted a pilot study with five participants. Additionally, we provide preliminary design guidelines informed by our user study to support the development of future VR-based speech trainers.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104160"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global Recurrent Mask R-CNN: Marine ship instance segmentation R-CNN：船舶实例分割

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104112

Ming Yuan, Hao Meng, Junbao Wu, Shouwen Cai

{"title":"Global Recurrent Mask R-CNN: Marine ship instance segmentation","authors":"Ming Yuan, Hao Meng, Junbao Wu, Shouwen Cai","doi":"10.1016/j.cag.2024.104112","DOIUrl":"10.1016/j.cag.2024.104112","url":null,"abstract":"<div><div>In intelligent ship navigation, instance segmentation technology is considered an accurate and efficient tool for vision perception in marine scenarios. However, the complex sea surface background and the diversity of ship types and sizes in marine environments pose significant challenges for instance segmentation, especially for small-scale targets. Therefore, this paper presents an end-to-end Global Recurrent Mask R-CNN (GR R-CNN) algorithm designed to enhance the multi-scale segmentation performance of ship instances in marine settings. Initially, this method proposes the Recurrent Enhanced Feature Pyramid Network (RE-FPN) module, which uses a feature recurrence and bidirectional chaining fusion mechanism to deeply integrate both deep and shallow features of images, effectively extracting multi-scale features and semantic information. Subsequently, we propose the Fine-Grained Global Fusion Mask Head (FGFMH) module, utilizing a fine-grained multi-layer receptive field extraction mechanism to enhance the extraction of global and multi-scale features. These two modules collaborate to further improve the ship instance segmentation capability. Experiments conducted on the MS COCO test-dev, PASCAL VOC, and custom OVSD datasets demonstrate accuracy improvements of 1.8%, 3.29%, and 1.3%, respectively, compared to Mask R-CNN. Our method surpasses various advanced techniques and provides valuable insights for the research on multi-scale instance segmentation of ships in complex environments.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104112"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-light image enhancement via illumination optimization and color correction 低光图像增强通过照明优化和色彩校正

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104138

Wenbo Zhang , Liang Xu , Jianjun Wu , Wei Huang , Xiaofan Shi , Yanli Li

{"title":"Low-light image enhancement via illumination optimization and color correction","authors":"Wenbo Zhang , Liang Xu , Jianjun Wu , Wei Huang , Xiaofan Shi , Yanli Li","doi":"10.1016/j.cag.2024.104138","DOIUrl":"10.1016/j.cag.2024.104138","url":null,"abstract":"<div><div>The issue of low-light image enhancement is investigated in this paper. Specifically, a trainable low-light image enhancer based on illumination optimization and color correction, called LLOCNet, is proposed to enhance the visibility of such low-light image. First, an illumination correction network is designed, leveraging residual and encoding-decoding structure, to correct the illumination information of the <span><math><mi>V</mi></math></span>-channel for lighting up the low-light image. After that, the illumination difference map is derived by difference between before and after luminance correction. Furthermore, an illumination-guided color correction network based on illumination-guided multi-head attention is developed to fine-tune the <span><math><mrow><mi>H</mi><mi>S</mi></mrow></math></span> color channels. Finally, a feature fusion block with asymmetric parallel convolution operation is adopted to reconcile these enhanced features to obtain the desired high-quality image. Both qualitative and quantitative experimental results show that the proposed network favorably performs against other state-of-the-art low-light enhancement methods on both real-world and synthetic low-light image dataset.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104138"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CtrlNeRF: The generative neural radiation fields for the controllable synthesis of high-fidelity 3D-aware images CtrlNeRF：用于高保真3d感知图像可控合成的生成神经辐射场

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2025.104163

Jian Liu , Zhen Yu

{"title":"CtrlNeRF: The generative neural radiation fields for the controllable synthesis of high-fidelity 3D-aware images","authors":"Jian Liu , Zhen Yu","doi":"10.1016/j.cag.2025.104163","DOIUrl":"10.1016/j.cag.2025.104163","url":null,"abstract":"<div><div>The neural radiance field (NERF) advocates learning the continuous representation of 3D geometry through a multilayer perceptron (MLP). By integrating this into a generative model, the generative neural radiance field (GRAF) is capable of producing images from random noise <span><math><mi>z</mi></math></span> without 3D supervision. In practice, the shape and appearance are modeled by <span><math><msub><mrow><mi>z</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>z</mi></mrow><mrow><mi>a</mi></mrow></msub></math></span>, respectively, to manipulate them separately during inference. However, it is challenging to represent multiple scenes using a solitary MLP and precisely control the generation of 3D geometry in terms of shape and appearance. In this paper, we introduce a controllable generative model (<span><math><mrow><mi>i</mi><mo>.</mo><mi>e</mi><mo>.</mo></mrow></math></span> <strong>CtrlNeRF</strong>) that uses a single MLP network to represent multiple scenes with shared weights. Consequently, we manipulated the shape and appearance codes to realize the controllable generation of high-fidelity images with 3D consistency. Moreover, the model enables the synthesis of novel views that do not exist in the training sets via camera pose alteration and feature interpolation. Extensive experiments were conducted to demonstrate its superiority in 3D-aware image generation compared to its counterparts.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104163"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Augmented Reality for Heavy Machinery Using Laser Projections 使用激光投影的重型机械空间增强现实

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104161

Maximilian Tschulik, Thomas Kernbauer, Philipp Fleck, Clemens Arth

{"title":"Spatial Augmented Reality for Heavy Machinery Using Laser Projections","authors":"Maximilian Tschulik, Thomas Kernbauer, Philipp Fleck, Clemens Arth","doi":"10.1016/j.cag.2024.104161","DOIUrl":"10.1016/j.cag.2024.104161","url":null,"abstract":"<div><div>Operating heavy machinery is challenging and requires the full attention of the operator to perform several complex tasks simultaneously. Although commonly used augmented reality (AR) devices, such as head-mounted or head-up displays, can provide occupational support to operators, they can also cause problems. Particularly in off-highway scenarios, i.e., when driving machines in bumpy environments, the usefulness of current AR devices and the willingness of operators to wear them are limited. Therefore, we explore how laser-projection-based AR can help the operators facilitate their tasks under real-world outdoor conditions. For this, we present a compact hardware unit and introduce a flexible and declarative software system. Furthermore, we examine the calibration process to leverage a camera projector setup and outline a process for creating images suitable for display by a laser projector from a set of line segments. We showcase its ability to provide efficient instructions to operators and bystanders and propose concrete applications for our setup. Finally, we perform an accuracy evaluation and test our system hands-on in snow grooming.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104161"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0