CAREER: Categorical Shape Reconstruction

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。解决这一问题的传统方法需要在不同的视角和/或照明下拍摄相同对象的多幅图像；这一要求对这些方法的操作范围造成了很大的限制。例如，在消费者摄影中，大多数照片的拍摄都不是出于这样的目的。即使在研究界，收集多视角/光照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颖的3D重建方法，这些方法对一个类别中相似但不相同的对象的图像集合进行操作，并恢复该类别的参数化3D模型族。所做的基本假设是，形状分布只具有有限的自由度，远远少于该类别可用的图像数据。这种方法利用了互联网上丰富的图像，这些图像密集地采样不同类别的相似对象的外观，如人脸、车辆、建筑、人类和动物。这项研究的结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，而不需要在受控环境中将所有单独的对象放置在转盘上。这些数据库具有广泛的影响，如自主导航、安全监视、人机交互、生物识别、图形、工业设计、虚拟环境、法医学、考古学和娱乐。PI还试图将这一研究项目的核心理念融入他的教育计划中。特别是，他的主要教育目标--无论是在课程作业方面，还是在指导研究生方面--都是强调数据驱动的愿景方法，这既是基础的，也是实际重要的。将及时分发数据和工具。Http://www.cs.wisc.edu/~lizhang将提供更新

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