Computational Visual Media最新文献_第4页

Multi-scale hash encoding based neural geometry representation 基于神经几何表示的多尺度哈希编码

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-03-22 DOI: 10.1007/s41095-023-0340-x

{"title":"Multi-scale hash encoding based neural geometry representation","authors":"","doi":"10.1007/s41095-023-0340-x","DOIUrl":"https://doi.org/10.1007/s41095-023-0340-x","url":null,"abstract":"<h3>Abstract</h3> Recently, neural implicit function-based representation has attracted more and more attention, and has been widely used to represent surfaces using differentiable neural networks. However, surface reconstruction from point clouds or multi-view images using existing neural geometry representations still suffer from slow computation and poor accuracy. To alleviate these issues, we propose a multi-scale hash encoding-based neural geometry representation which effectively and efficiently represents the surface as a signed distance field. Our novel neural network structure carefully combines low-frequency Fourier position encoding with multi-scale hash encoding. The initialization of the geometry network and geometry features of the rendering module are accordingly redesigned. Our experiments demonstrate that the proposed representation is at least 10 times faster for reconstructing point clouds with millions of points. It also significantly improves speed and accuracy of multi-view reconstruction. Our code and models are available at https://github.com/Dengzhi-USTC/Neural-Geometry-Reconstruction. <img alt=\"\" src=\"https://static-content.springer.com/image/MediaObjects/41095_2023_340_Fig1_HTML.jpg\"/> ","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"8 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Erratum to: Dynamic ocean inverse modeling based on differentiable rendering 勘误：基于可变渲染的动态海洋逆建模

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-03-22 DOI: 10.1007/s41095-024-0398-z

Xueguang Xie, Yang Gao, Fei Hou, Aimin Hao, Hong Qin

引用次数: 0

Delving into high-quality SVBRDF acquisition: A new setup and method 深入研究高质量 SVBRDF 采集：新的设置和方法

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-09 DOI: 10.1007/s41095-023-0352-6

Chuhua Xian, Jiaxin Li, Hao Wu, Zisen Lin, Guiqing Li

引用次数: 0

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network CF-DAN：基于交叉融合双注意网络的面部表情识别

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-08 DOI: 10.1007/s41095-023-0369-x

{"title":"CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network","authors":"","doi":"10.1007/s41095-023-0369-x","DOIUrl":"https://doi.org/10.1007/s41095-023-0369-x","url":null,"abstract":"<h3>Abstract</h3> Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C2 activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks. <img alt=\"\" src=\"https://static-content.springer.com/image/MediaObjects/41095_2023_369_Fig1_HTML.jpg\"/> ","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"17 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-task learning and joint refinement between camera localization and object detection 多任务学习以及摄像机定位和物体检测之间的联合改进

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-08 DOI: 10.1007/s41095-022-0319-z

Junyi Wang, Yue Qi

{"title":"Multi-task learning and joint refinement between camera localization and object detection","authors":"Junyi Wang, Yue Qi","doi":"10.1007/s41095-022-0319-z","DOIUrl":"https://doi.org/10.1007/s41095-022-0319-z","url":null,"abstract":"Visual localization and object detection both play important roles in various tasks. In many indoor application scenarios where some detected objects have fixed positions, the two techniques work closely together. However, few researchers consider these two tasks simultaneously, because of a lack of datasets and the little attention paid to such environments. In this paper, we explore multi-task network design and joint refinement of detection and localization. To address the dataset problem, we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic process. The dataset provides localization and detection information, and is publicly available at https://drive.google.com/drive/folders/1U28zkuN4_I0dbzkqyIAKlAl5k9oUK0jI?usp=sharing for benchmarking localization and object detection tasks. Targeting this dataset, we have designed a multi-task network, JLDNet, based on YOLO v3, that outputs a target point cloud and object bounding boxes. For dynamic environments, the detection branch also promotes the perception of dynamics. JLDNet includes image feature learning, point feature learning, feature fusion, detection construction, and point cloud regression. Moreover, object-level bundle adjustment is used to further improve localization and detection accuracy. To test JLDNet and compare it to other methods, we have conducted experiments on 7 static scenes, our constructed dataset, and the dynamic TUM RGB-D and Bonn datasets. Our results show state-of-the-art accuracy for both tasks, and the benefit of jointly working on both tasks is demonstrated.\u0000","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"4 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DualSmoke: Sketch-based smoke illustration design with two-stage generative model DualSmoke：基于草图的烟雾插图设计与两阶段生成模型

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-08 DOI: 10.1007/s41095-022-0318-0

Haoran Xie, Keisuke Arihara, Syuhei Sato, Kazunori Miyata

引用次数: 0

Deep panoramic depth prediction and completion for indoor scenes 室内场景的深度全景深度预测和完成

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-08 DOI: 10.1007/s41095-023-0358-0

Giovanni Pintore, Eva Almansa, Armando Sanchez, Giorgio Vassena, Enrico Gobbetti

{"title":"Deep panoramic depth prediction and completion for indoor scenes","authors":"Giovanni Pintore, Eva Almansa, Armando Sanchez, Giorgio Vassena, Enrico Gobbetti","doi":"10.1007/s41095-023-0358-0","DOIUrl":"https://doi.org/10.1007/s41095-023-0358-0","url":null,"abstract":"We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning.\u0000","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"3 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Shape embedding and retrieval in multi-flow deformation 多流变形中的形状嵌入和检索

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-02-08 DOI: 10.1007/s41095-022-0315-3

Baiqiang Leng, Jingwei Huang, Guanlin Shen, Bin Wang

{"title":"Shape embedding and retrieval in multi-flow deformation","authors":"Baiqiang Leng, Jingwei Huang, Guanlin Shen, Bin Wang","doi":"10.1007/s41095-022-0315-3","DOIUrl":"https://doi.org/10.1007/s41095-022-0315-3","url":null,"abstract":"We propose a unified 3D flow framework for joint learning of shape embedding and deformation for different categories. Our goal is to recover shapes from imperfect point clouds by fitting the best shape template in a shape repository after deformation. Accordingly, we learn a shape embedding for template retrieval and a flow-based network for robust deformation. We note that the deformation flow can be quite different for different shape categories. Therefore, we introduce a novel multi-hub module to learn multiple modes of deformation to incorporate such variation, providing a network which can handle a wide range of objects from different categories. The shape embedding is designed to retrieve the best-fit template as the nearest neighbor in a latent space. We replace the standard fully connected layer with a tiny structure in the embedding that significantly reduces network complexity and further improves deformation quality. Experiments show the superiority of our method to existing state-of-the-art methods via qualitative and quantitative comparisons. Finally, our method provides efficient and flexible deformation that can further be used for novel shape design.\u0000","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"45 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Dynamic ocean inverse modeling based on differentiable rendering 基于可变渲染的动态海洋反演建模

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-01-03 DOI: 10.1007/s41095-023-0338-4

Xueguang Xie, Yang Gao, Fei Hou, Aimin Hao, Hong Qin

{"title":"Dynamic ocean inverse modeling based on differentiable rendering","authors":"Xueguang Xie, Yang Gao, Fei Hou, Aimin Hao, Hong Qin","doi":"10.1007/s41095-023-0338-4","DOIUrl":"https://doi.org/10.1007/s41095-023-0338-4","url":null,"abstract":"Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation. To bridge the technical gap between virtual and real environments, we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans, taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner. First, we infer hierarchical geometry using two networks, which are optimized via the differentiable renderer. We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model. Then, ocean dynamics can be evolved using the reconstructed wave components. Through extensive experiments, we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation. Moreover, the new framework has the inverse modeling potential to facilitate a host of graphics applications, such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.\u0000","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"72 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Benchmarking visual SLAM methods in mirror environments 镜像环境中视觉 SLAM 方法的基准测试

IF 6.9 3区计算机科学

Computational Visual Media Pub Date : 2024-01-03 DOI: 10.1007/s41095-022-0329-x

Peter Herbert, Jing Wu, Ze Ji, Yu-Kun Lai

{"title":"Benchmarking visual SLAM methods in mirror environments","authors":"Peter Herbert, Jing Wu, Ze Ji, Yu-Kun Lai","doi":"10.1007/s41095-022-0329-x","DOIUrl":"https://doi.org/10.1007/s41095-022-0329-x","url":null,"abstract":"Visual simultaneous localisation and mapping (vSLAM) finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities, particularly mirror reflections. The effect of mirror presence (time visible and its average size in the frame) was hypothesised to impact localisation and mapping performance, with systems using direct techniques expected to perform worse. Thus, a dataset, MirrEnv, of image sequences recorded in mirror environments, was collected, and used to evaluate the performance of existing representative methods. RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration, whilst the remaining results did not show significantly degraded localisation performance. The mesh maps generated proved to be very inaccurate, with real and virtual reflections colliding in the reconstructions. A discussion is given of the likely sources of error and robustness in mirror environments, outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors. The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.\u0000","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"39 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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