Computer Graphics Forum最新文献_第5页

A TransISP Based Image Enhancement Method for Visual Disbalance in Low-light Images 基于 TransISP 的弱光图像视觉失衡增强方法

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15209

Jiaqi Wu, Jing Guo, Rui Jing, Shihao Zhang, Zijian Tian, Wei Chen, Zehua Wang

{"title":"A TransISP Based Image Enhancement Method for Visual Disbalance in Low-light Images","authors":"Jiaqi Wu, Jing Guo, Rui Jing, Shihao Zhang, Zijian Tian, Wei Chen, Zehua Wang","doi":"10.1111/cgf.15209","DOIUrl":"https://doi.org/10.1111/cgf.15209","url":null,"abstract":"Existing image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low-light images. To overcome these challenges, we propose a TransISP-based image enhancement method specifically designed for low-light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long-distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low-light images captured from real-world coal mine underground scenarios.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"43 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665174","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

Surface Cutting and Flattening to Target Shapes 根据目标形状进行表面切割和压平

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15223

Yuanhao Li, Wenzheng Wu, Ligang Liu

引用次数: 0

Adversarial Unsupervised Domain Adaptation for 3D Semantic Segmentation with 2D Image Fusion of Dense Depth 利用密集深度的二维图像融合进行三维语义分割的对抗性无监督领域自适应

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15250

Xindan Zhang, Ying Li, Huankun Sheng, Xinnian Zhang

{"title":"Adversarial Unsupervised Domain Adaptation for 3D Semantic Segmentation with 2D Image Fusion of Dense Depth","authors":"Xindan Zhang, Ying Li, Huankun Sheng, Xinnian Zhang","doi":"10.1111/cgf.15250","DOIUrl":"https://doi.org/10.1111/cgf.15250","url":null,"abstract":"Unsupervised domain adaptation (UDA) is increasingly used for 3D point cloud semantic segmentation tasks due to its ability to address the issue of missing labels for new domains. However, most existing unsupervised domain adaptation methods focus only on uni-modal data and are rarely applied to multi-modal data. Therefore, we propose a cross-modal UDA on multi-modal datasets that contain 3D point clouds and 2D images for 3D Semantic Segmentation. Specifically, we first propose a Dual discriminator-based Domain Adaptation (Dd-bDA) module to enhance the adaptability of different domains. Second, given that the robustness of depth information to domain shifts can provide more details for semantic segmentation, we further employ a Dense depth Feature Fusion (DdFF) module to extract image features with rich depth cues. We evaluate our model in four unsupervised domain adaptation scenarios, i.e., dataset-to-dataset (A2D2 → SemanticKITTI), Day-to-Night, country-to-country (USA → Singapore), and synthetic-to-real (VirtualKITTI → SemanticKITTI). In all settings, the experimental results achieve significant improvements and surpass state-of-the-art models.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"43 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665133","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

Symmetric Piecewise Developable Approximations 对称分片可展开近似法

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15242

Ying He, Qing Fang, Zheng Zhang, Tielin Dai, Kang Wu, Ligang Liu, Xiao-Ming Fu

引用次数: 0

GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting GS-Octree：基于八叉树的三维高斯拼接技术，用于强光下稳健的物体级三维重建

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15206

J. Li, Z. Wen, L. Zhang, J. Hu, F. Hou, Z. Zhang, Y. He

{"title":"GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting","authors":"J. Li, Z. Wen, L. Zhang, J. Hu, F. Hou, Z. Zhang, Y. He","doi":"10.1111/cgf.15206","DOIUrl":"https://doi.org/10.1111/cgf.15206","url":null,"abstract":"The 3D Gaussian Splatting technique has significantly advanced the construction of radiance fields from multi-view images, enabling real-time rendering. While point-based rasterization effectively reduces computational demands for rendering, it often struggles to accurately reconstruct the geometry of the target object, especially under strong lighting conditions. Strong lighting can cause significant color variations on the object's surface when viewed from different directions, complicating the reconstruction process. To address this challenge, we introduce an approach that combines octree-based implicit surface representations with Gaussian Splatting. Initially, it reconstructs a signed distance field (SDF) and a radiance field through volume rendering, encoding them in a low-resolution octree. This initial SDF represents the coarse geometry of the target object. Subsequently, it introduces 3D Gaussians as additional degrees of freedom, which are guided by the initial SDF. In the third stage, the optimized Gaussians enhance the accuracy of the SDF, enabling the recovery of finer geometric details compared to the initial SDF. Finally, the refined SDF is used to further optimize the 3D Gaussians via splatting, eliminating those that contribute little to the visual appearance. Experimental results show that our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs more accurate geometry, particularly in images with specular highlights caused by strong lighting. The source code can be downloaded from https://github.com/LaoChui999/GS-Octree.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"43 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665172","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

Faster Ray Tracing through Hierarchy Cut Code 通过层次剪切代码实现更快的光线跟踪

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15226

WeiLai Xiang, FengQi Liu, Zaonan Tan, Dan Li, PengZhan Xu, MeiZhi Liu, QiLong Kou

引用次数: 0

Disentangled Lifespan Synthesis via Transformer-Based Nonlinear Regression 通过基于变压器的非线性回归进行分解寿命合成

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15229

Mingyuan Li, Yingchun Guo

{"title":"Disentangled Lifespan Synthesis via Transformer-Based Nonlinear Regression","authors":"Mingyuan Li, Yingchun Guo","doi":"10.1111/cgf.15229","DOIUrl":"https://doi.org/10.1111/cgf.15229","url":null,"abstract":"Lifespan face age transformation aims to generate facial images that accurately depict an individual's appearance at different age stages. This task is highly challenging due to the need for reasonable changes in facial features while preserving identity characteristics. Existing methods tend to synthesize unsatisfactory results, such as entangled facial attributes and low identity preservation, especially when dealing with large age gaps. Furthermore, over-manipulating the style vector may deviate it from the latent space and damage image quality. To address these issues, this paper introduces a novel nonlinear regression model-Disentangled Lifespan face Aging (DL-Aging) to achieve high-quality age transformation images. Specifically, we propose an age modulation encoder to extract age-related multi-scale facial features as key and value, and use the reconstructed style vector of the image as the query. The multi-head cross-attention in the W+ space is utilized to update the query for aging image reconstruction iteratively. This nonlinear transformation enables the model to learn a more disentangled mode of transformation, which is crucial for alleviating facial attribute entanglement. Additionally, we introduce a W+ space age regularization term to prevent excessive manipulation of the style vector and ensure it remains within the W+ space during transformation, thereby improving generation quality and aging accuracy. Extensive qualitative and quantitative experiments demonstrate that the proposed DL-Aging outperforms state-of-the-art methods regarding aging accuracy, image quality, attribute disentanglement, and identity preservation, especially for large age gaps.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"43 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665135","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

Density-Aware Diffusion Model for Efficient Image Dehazing 用于高效图像去重的密度感知扩散模型

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15221

Ling Zhang, Wenxu Bai, Chunxia Xiao

{"title":"Density-Aware Diffusion Model for Efficient Image Dehazing","authors":"Ling Zhang, Wenxu Bai, Chunxia Xiao","doi":"10.1111/cgf.15221","DOIUrl":"https://doi.org/10.1111/cgf.15221","url":null,"abstract":"Existing image dehazing methods have made remarkable progress. However, they generally perform poorly on images with dense haze, and often suffer from unsatisfactory results with detail degradation or color distortion. In this paper, we propose a density-aware diffusion model (DADM) for image dehazing. Guided by the haze density, our DADM can handle images with dense haze and complex environments. Specifically, we introduce a density-aware dehazing network (DADNet) in the reverse diffusion process, which can help DADM gradually recover a clear haze-free image from a haze image. To improve the performance of the network, we design a cross-feature density extraction module (CDEModule) to extract the haze density for the image and a density-guided feature fusion block (DFFBlock) to learn the effective contextual features. Furthermore, we introduce an indirect sampling strategy in the test sampling process, which not only suppresses the accumulation of errors but also ensures the stability of the results. Extensive experiments on popular benchmarks validate the superior performance of the proposed method. The code is released in https://github.com/benchacha/DADM.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"43 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665130","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

Curved Image Triangulation Based on Differentiable Rendering 基于可微分渲染的曲面图像三角测量

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15232

Wanyi Wang, Zhonggui Chen, Lincong Fang, Juan Cao

引用次数: 0

TaNSR:Efficient 3D Reconstruction with Tetrahedral Difference and Feature Aggregation TaNSR：利用四面体差分和特征聚合实现高效三维重建

IF 2.7 4区计算机科学

Computer Graphics Forum Pub Date : 2024-10-24 DOI: 10.1111/cgf.15207

Zhaohan Lv, Xingcan Bao, Yong Tang, Jing Zhao

引用次数: 0