CAREER: Categorical Shape Reconstruction

从物体的二维图像中重建物体的三维几何模型一直是计算视觉中一个长期存在的问题。解决这一问题的传统方法需要在不同视角和/或光照下拍摄相同物体的许多图像；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，大多数照片都没有考虑到这一目的。即使在研究界，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一种新的3D重建方法，这种方法可以对一个类别中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的3D模型。所做的基本假设是形状分布只有有限的自由度，比类别可用的图像数据少得多。这种方法利用了互联网上大量的图像，这些图像密集地采样了不同类别（如人脸、车辆、建筑、人类和动物）中相似对象的外观。这项研究的结果可以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在受控设置的转盘上。此类数据库具有广泛的影响，例如自主导航、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还试图将该研究项目的核心思想融入他的教育计划中。特别是，他的主要教育目标——无论是在课程作业中还是在指导研究生时——都是强调一种数据驱动的方法，这种方法既基础又重要。将及时传播这些数据和工具。更新将从http://www.cs.wisc.edu/~lizhang获得

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