Collaborative Research: Recovery of 3D Shapes from Single Views

合作研究：从单一视图恢复3D图像Zygmunt Pizlo，普渡大学Longin Jan Latecki，天普大学人眼，像一台相机，产生一个3维世界的2维图像。人类的大脑是如何成功地解释这些贫乏的二维图像，让我们看到世界实际上是“在那里？这个重要的基本问题，300年来一直受到重视，尽管许多科学家、工程师和数学家做出了努力，但仍然没有得到答案。尚未成功的传统方法试图通过分析多个图像（诸如可以从两只眼睛或从移动图像获得）中的表面的深度并且通过强调学习和熟悉的作用来从对象和场景的二维图像恢复对象和场景的三维形状。普渡大学的齐格蒙特·皮兹洛（Zygmunt Pizlo）和坦普尔大学的朗金·扬·拉泰茨基（Longin Jan Latecki）采取的方法截然不同。它仅使用单个2-D图像通过应用先验约束（关于人类视觉系统内置的世界的假设）来恢复第三维，这些先验约束反映了通常存在于物理世界中的重要视觉属性，例如3D对象的对称性和紧凑性。Pizlo和Latecki的研究有可能鼓励人类感知研究的理论变革，因为它使用了一种全新的方法来解决视觉中的经典未解决问题。它可以支持机器视觉的突破，因为人类比任何机器都更擅长从2D信息中恢复3D世界。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的研究试图通过将人类观察者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，计算机科学和工程的跨学科教育提供一个很好的机会。

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.