Collaborative Research: Recovery of 3D Shapes From Single Views

合作研究：从单视图中恢复3D形状普渡大学的Zygmunt Pizlo普渡大学的Longin Jan Latecki，坦普尔大学人眼就像相机一样，产生三维世界的二维图像。人脑如何成功地解释这些贫乏的二维图像，让我们看到世界的真实面貌？尽管许多科学家、工程师和数学家作出了努力，但这个意义已被认识了300年的根本问题仍未得到解答。尚未成功的传统方法试图通过分析多个图像中表面的深度(例如，可能从两只眼睛或从运动图像中获得)并通过强调学习和熟悉的作用来从其二维图像中恢复对象和场景的三维形状。普渡大学的Zygmunt Pizlo和坦普尔大学的Longin Jan Latecki采取的方法截然不同。它只使用一幅2-D图像，通过应用先验约束(人类视觉系统中内置的关于世界的假设)来恢复第三维，这些约束反映了通常存在于物理世界中的重要视觉属性，如3D对象的对称性和紧凑性。Pizlo和Latecki的研究有可能鼓励人类知觉研究中的理论变革，因为它使用了一种全新的方法来解决视觉中一个经典的悬而未决的问题。它可以支持机器视觉的突破，因为众所周知，人类在从2D信息中恢复3D世界方面比任何机器都要好得多。机器视觉在许多领域都有重要的应用，包括执法和国家安全。Pizlo和Latecki的研究试图通过将在人类观察者身上进行的实验结果与最先进的计算建模相结合来解决视觉3D形状问题。该项目将为研究生和本科生在心理学、计算机科学和工程方面的跨学科教育提供一个极好的机会。

Collaborative Research: Recovery of 3D Shapes from Single ViewsZygmunt Pizlo, Purdue UniversityLongin Jan Latecki, Temple UniversityThe human eye, like a camera, produces 2-dimensional images of a 3-dimensional world. How does the human brain succeed in interpreting these impoverished 2-dimensional images, allowing us to see the world as it actually is "out there?" This fundamental question, whose significance has been appreciated for 300 years, has not been answered despite the efforts of many scientists, engineers and mathematicians. Conventional approaches, which have not been successful, tried to recover the 3-dimensional shapes of objects and scenes from their 2-dimensional images by analyzing the depths of surfaces in multiple images (such as might be obtained from two eyes or from moving images) and by emphasizing the role of learning and familiarity. The approach taken by Zygmunt Pizlo at Purdue University and Longin Jan Latecki at Temple University is very different. It uses only a single 2-D image to recover the third dimension by applying a priori constraints (assumptions about the world built-in to the human visual system) that reflect important visual properties that are generally present in the physical world, properties such as the symmetry and compactness of 3D objects. Pizlo and Latecki's research has the potential of encouraging theoretical changes in the study of human perception because it uses an entirely new approach to a classical unsolved problem in vision. It could support breakthroughs in machine vision because human beings are known to be much better than any machine confronted with recovering the 3D world from 2D information. Machine vision has important applications to many domains, including law enforcement and national security. Pizlo and Latecki's research attempts to solve the visual 3D shape problem by combining the results of experiments on human observers with state-of-the-art computational modeling. The project will provide an excellent opportunity for the interdisciplinary education of graduate and undergraduate students in psychology, computer science and engineering.