Converting 2D Video to 3D with Applications to 3D-TV, Video Analysis and Compression

通过 3D 电视、视频分析和压缩应用将 2D 视频转换为 3D

• 批准号: 0313047
• 负责人: Marc Pollefeys
• 金额: $ 28万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0313047&HistoricalAwards=false
• 关键词: Converting; 2D; Video; 3D; Applications

The aim of this project is to augment traditional 2D-video content with depth using computer vision approaches. To achieve this goal the camera motion and settings will also have to be recovered from the video data. The main application that is envisioned is converting existing 2D-video to 3D-video to provide sufficient content for stereoscopic displays and enable applications such 3D-TV to emerge and flourish. Besides this, the intended research potentially also has important applications in the context of video analysis and compression. Advances in 3D from video will also have a broader impact in areas such as archaeology, cultural heritage, movie special effects, medical, forensics and military reconnaissance applications. The educational impact will not be limited to students directly involved in this project, but will potentially reach many more through tools for video analysis in art schools or educational 3D-videos. We intend to develop a reliable fully automatic approach. Since it will not always be possible to compute the depth from the available image content (e.g. fixed camera), we intend to correctly deal with ambiguities and provide perceptually acceptable results (e.g. fade depth when it can't be computed anymore). Given a 2D video stream, we intend to (1) compute the relative motion between the scene and the camera for each shot, (2) detect independent moving objects and computer their motion and deformation, (3) compute a detailed depth representation for each video.

该项目的目的是使用计算机视觉方法增强传统的2D视频内容的深度。 为了实现这一目标，还必须从视频数据中恢复摄像机运动和设置。设想的主要应用是将现有的2D视频转换为3D视频，以提供足够的立体显示内容，并使3D TV等应用能够出现和蓬勃发展。 除此之外，预期的研究可能在视频分析和压缩方面也有重要的应用。视频3D技术的进步也将在考古学、文化遗产、电影特效、医学、法医学和军事侦察应用等领域产生更广泛的影响。 教育影响将不仅限于直接参与该项目的学生，还可能通过艺术学校的视频分析工具或教育3D视频达到更多人。我们打算开发一种可靠的全自动方法。由于并不总是能够从可用的图像内容（例如固定的相机）计算深度，因此我们打算正确地处理模糊性并提供感知上可接受的结果（例如，当不能再计算时，渐变深度）。给定一个2D视频流，我们打算（1）计算每个镜头的场景和相机之间的相对运动，（2）检测独立的移动对象并计算它们的运动和变形，（3）计算每个视频的详细深度表示。