基于实时距离场加速的大型运动场自由视点视频合成

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2024-01-03 DOI:10.1007/s41095-022-0323-3
Yanran Dai, Jing Li, Yuqi Jiang, Haidong Qin, Bang Liang, Shikuan Hong, Haozhe Pan, Tao Yang
{"title":"基于实时距离场加速的大型运动场自由视点视频合成","authors":"Yanran Dai, Jing Li, Yuqi Jiang, Haidong Qin, Bang Liang, Shikuan Hong, Haozhe Pan, Tao Yang","doi":"10.1007/s41095-022-0323-3","DOIUrl":null,"url":null,"abstract":"<p>Free-viewpoint video allows the user to view objects from any virtual perspective, creating an immersive visual experience. This technology enhances the interactivity and freedom of multimedia performances. However, many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision, particularly for sports fields having large areas and numerous moving objects. To address these issues, we propose a free-viewpoint video synthesis method based on distance field acceleration. The central idea is to fuse multi-view distance field information and use it to adjust the search step size adaptively. Adaptive step size search is used in two ways: for fast estimation of multi-object three-dimensional surfaces, and synthetic view rendering based on global occlusion judgement. We have implemented our ideas using parallel computing for interactive display, using CUDA and OpenGL frameworks, and have used real-world and simulated experimental datasets for evaluation. The results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 fps. Furthermore, the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.\n</p>","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":"12 1","pages":""},"PeriodicalIF":17.3000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields\",\"authors\":\"Yanran Dai, Jing Li, Yuqi Jiang, Haidong Qin, Bang Liang, Shikuan Hong, Haozhe Pan, Tao Yang\",\"doi\":\"10.1007/s41095-022-0323-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Free-viewpoint video allows the user to view objects from any virtual perspective, creating an immersive visual experience. This technology enhances the interactivity and freedom of multimedia performances. However, many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision, particularly for sports fields having large areas and numerous moving objects. To address these issues, we propose a free-viewpoint video synthesis method based on distance field acceleration. The central idea is to fuse multi-view distance field information and use it to adjust the search step size adaptively. Adaptive step size search is used in two ways: for fast estimation of multi-object three-dimensional surfaces, and synthetic view rendering based on global occlusion judgement. We have implemented our ideas using parallel computing for interactive display, using CUDA and OpenGL frameworks, and have used real-world and simulated experimental datasets for evaluation. The results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 fps. Furthermore, the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.\\n</p>\",\"PeriodicalId\":37301,\"journal\":{\"name\":\"Computational Visual Media\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":17.3000,\"publicationDate\":\"2024-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Visual Media\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s41095-022-0323-3\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Visual Media","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s41095-022-0323-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

自由视角视频允许用户从任何虚拟视角观看物体,创造身临其境的视觉体验。这项技术增强了多媒体表演的互动性和自由度。然而,许多自由视点视频合成方法很难满足高精度实时工作的要求,尤其是对于面积大、移动物体多的运动场地。为了解决这些问题,我们提出了一种基于距离场加速的自由视点视频合成方法。其核心思想是融合多视角距离场信息,并利用这些信息自适应地调整搜索步长。自适应步长搜索有两种用途:快速估计多物体三维表面和基于全局遮挡判断的合成视图渲染。我们利用 CUDA 和 OpenGL 框架,通过并行计算实现了我们的想法,并使用真实世界和模拟实验数据集进行评估。结果表明,所提出的方法可以在大型运动场上以 25 fps 的速度渲染包含多个物体的自由视点视频。此外,我们合成的新视角图像的视觉质量超过了最先进的基于神经渲染的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

Free-viewpoint video allows the user to view objects from any virtual perspective, creating an immersive visual experience. This technology enhances the interactivity and freedom of multimedia performances. However, many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision, particularly for sports fields having large areas and numerous moving objects. To address these issues, we propose a free-viewpoint video synthesis method based on distance field acceleration. The central idea is to fuse multi-view distance field information and use it to adjust the search step size adaptively. Adaptive step size search is used in two ways: for fast estimation of multi-object three-dimensional surfaces, and synthetic view rendering based on global occlusion judgement. We have implemented our ideas using parallel computing for interactive display, using CUDA and OpenGL frameworks, and have used real-world and simulated experimental datasets for evaluation. The results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 fps. Furthermore, the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
期刊最新文献
TrafPS: A shapley-based visual analytics approach to interpret traffic CLIP-Flow: Decoding images encoded in CLIP space CLIP-SP: Vision-language model with adaptive prompting for scene parsing SGformer: Boosting transformers for indoor lighting estimation from a single image Central similarity consistency hashing for asymmetric image retrieval
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1