RI: Medium: Collaborative Research: Physically Grounded Object Recognition

RI：媒介：协作研究：物理接地物体识别

• 批准号: 0905402
• 负责人: Martial Hebert
• 金额: $ 79.9万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905402&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Physically

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."Although the world is very much three-dimensional, most of today's approaches to visual object recognition essentially reduce the problem to one of 2D pattern classification, where rectangular image patches are independently compared to stored templates to produce isolated object labels within the image. This project aims to account for the three-dimensional nature of the real world by exploring qualitative geometric reasoning in terms of 3D spatial relationships between scene components, category-level object models, and global scene understanding.The project is organized around two major research areas. Qualitative 3D scene parsing: A central part of our effort will be to develop qualitative 3D models of the scene that describe the depicted objects and surfaces and their physical relations. Grounding objects in the scene: We integrate the geometric representation of the scene and the corresponding 3D spatial relations with the object recognition process by (1) inferring the set of likely object identities based on 3D relations among scene components; (2) predicting the most likely object locations from the scene layout; and (3) using the occlusion relations and depth ordering to predict the parts of objects that may be visible in the scene.The project is anticipated to result in major advances in 3D scene understanding from photographs, a critical enabling technology for a wide range of applications including autonomous systems, health care, human-computer interaction, assistive technology, image retrieval, industrial and personal robotics, manufacturing, scientific image analysis, surveillance and security, and transportation.

“这项奖励是根据2009年美国复苏和再投资法案（公法111-5）资助的。”虽然世界是非常三维的，但今天的大多数视觉对象识别方法本质上都将问题简化为二维模式分类之一，其中矩形图像补丁被独立地与存储的模板进行比较，以在图像中产生孤立的对象标签。该项目旨在通过探索场景组件之间的三维空间关系、类别级对象模型和全局场景理解方面的定性几何推理，来解释现实世界的三维本质。该项目围绕两个主要研究领域组织。定性3D场景解析：我们努力的核心部分将是开发描述所描绘对象和表面及其物理关系的场景定性3D模型。场景中的接地物体：我们将场景的几何表示及其对应的三维空间关系与物体识别过程相结合，通过(1)根据场景组件之间的三维关系推断出可能的物体身份集；(2)根据场景布局预测最可能出现的目标位置；(3)利用遮挡关系和深度排序来预测场景中可能可见的物体部分。该项目预计将在从照片中理解3D场景方面取得重大进展，这是广泛应用的关键使能技术，包括自主系统、医疗保健、人机交互、辅助技术、图像检索、工业和个人机器人、制造、科学图像分析、监视和安全以及运输。