CAREER: Categorical Shape Reconstruction

职业：分类形状重建

• 批准号: 0845916
• 负责人: Li Zhang
• 金额: $ 50万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845916&HistoricalAwards=false
• 关键词: CAREER; Categorical; Shape; Reconstruction

Reconstructing 3D geometric models of objects from their 2D images has been a longstanding problem in computational vision. Traditional approaches to this problem require many images taken of the same objects under different viewpoints and/or illuminations; this requirement poses a significant limitation on the operating range of these methods. For example, in consumer photography, the majority of photos are not taken with such a purpose in mind. Even in research communities, collecting multi-view/illumination image data sets is a tedious task. The PI and his team are investigating novel 3D reconstruction methods that operate on a collection of images for similar-but-not-identical objects in one category and recover a family of parameterized 3D models for the category. The underlying assumption made is that the shape distribution has only limited number degrees of freedom, many less than the image data that are available for the category. This approach exploits the abundance of images on the Internet that densely sample the appearance of similar objects in different categories, such as faces, vehicles, architectures, humans, and animals. The outcome of this research enables the creation of large-scale parameterized 3D model databases for these categories, without the need of placing all individual objects on a turntable in a controlled setting. Such databases have a wide range of impact, such as autonomous navigation, security surveillance, human computer interaction, biometrics, graphics, industry design, virtual environment, forensics, archeology, and entertainment. The PI also seeks to integrate the core idea of this research project into his education plan. In particular, his primary education goal-both in course work and in mentoring graduate students - is to emphasize a data - driven approach to vision that is both foundational and practically important. The data and tools will be disseminated in a timely fashion. Updates will be available from http://www.cs.wisc.edu/~lizhang

从物体的二维图像重建物体的三维几何模型一直是计算视觉中的一个长期问题。解决这个问题的传统方法需要在不同视角和/或照明下拍摄相同对象的许多图像;这种要求对这些方法的操作范围造成了显著的限制。例如，在消费者摄影中，大多数照片都不是带着这样的目的拍摄的。即使在研究社区中，收集多视图/照明图像数据集也是一项繁琐的任务。PI和他的团队正在研究新的3D重建方法，这些方法可以对一类相似但不相同的物体的图像集合进行操作，并为该类别恢复一系列参数化3D模型。所做的基本假设是形状分布只有有限的自由度，比可用于该类别的图像数据少得多。这种方法利用了互联网上大量的图像，这些图像密集地对不同类别中的相似对象的外观进行采样，例如人脸、车辆、建筑、人类和动物。这项研究的成果使得能够为这些类别创建大规模的参数化3D模型数据库，而不需要将所有单个对象放置在受控设置的转盘上。这些数据库具有广泛的影响，例如自主导航，安全监控，人机交互，生物识别，图形，工业设计，虚拟环境，取证，考古和娱乐。PI还寻求将该研究项目的核心理念融入到他的教育计划中。特别是，他的主要教育目标-无论是在课程工作和指导研究生-是强调一个数据驱动的方法来愿景，这是基础和实际重要的。这些数据和工具将及时分发。更新可从http://www.cs.wisc.edu/~lizhang获得