RI: Large: Collaborative Research: 3D Structure and Motion in Dynamic Natural Scenes

视觉系统如何从相机中的传感器或我们眼睛中的视网膜接收到的动态二维图像中恢复世界的三维结构--例如环境的布局、表面形状或物体运动？这个问题对于计算机和生物视觉来说都是基本的。计算机视觉已经开发了各种算法来估计场景的特定方面，例如可以建立其随时间的对应关系的点的三维位置，但是对于复杂的自然场景和观看条件，获得完整和健壮的场景表示仍然是一个挑战。生物视觉系统已经进化出令人印象深刻的能力，这表明它们具有对三维世界的详细和健壮的表示，但支持这一点的神经表示很少被理解，神经生理学研究到目前为止对计算过程的洞察也很少。该项目将采用跨学科的方法，试图了解三维场景分析的核心普遍原理。具体地说，该项目将1)开发一类新的计算模型，以恢复和表示三维场景信息，2)收集各种受控运动条件下动态自然场景的高质量视频和范围数据，以及3)在心理物理实验中测试这些模型的知觉含义。计算模型将利用非线性分解--即，根据多种因素的非线性相互作用来解释复杂的、随时间变化的图像的能力，例如观察者运动、三维场景布局和表面图案之间的相互作用。重要的是，这些模型的组件将根据观察者在自然场景中的运动和视点周围的运动而产生的自然运动模式的统计数据进行调整。该项目是三个实验室的合作，这三个实验室在开发自然图像统计、视觉神经表征和感知过程的理论模型方面发挥了主导作用。研究人员试图联合他们的努力，开发新的模型、数据集和三维自然场景结构的特征，这些模型、数据集和特征超出了以前对自然图像统计的研究，并可以在神经生理学和心理物理实验中进行测试。这个项目有可能通过开发新的模型类来有力地推断三维自然环境的表示，从而在神经科学、视觉感知和计算机视觉方面带来根本性的进步。它将创建一套高质量的数据库，以帮助其他调查人员研究这些问题。它还将为产生现实刺激开辟新的可能性，这些刺激可以指导神经表示和处理的新研究。

How does a vision system recover the 3-dimensional structure of the world -- such as the layout of the environment, surface shape, or object motion -- from the dynamic 2-dimensional images received by the sensors in a camera, or the retinas in our eyes? This problem is fundamental to both computer and biological vision. Computer vision has developed a variety of algorithms for estimating specific aspects of a scene such as the 3-dimensional positions of points whose correspondence over time can be established, but obtaining complete and robust scene representations for complex natural scenes and viewing conditions remains a challenge. Biological vision systems have evolved impressive capabilities that suggest they have detailed and robust representations of the 3-dimensional world, but the neural representations that subserve this are poorly understood and neurophysiological studies thus far have provided little insight into the computational process. This project will pursue an interdisciplinary approach by attempting the understand the universal principles that lie at the heart of 3-dimensional scene analysis.Specifically, the project will 1) develop a novel class of computational models that recover and represent 3-dimensional scene information, 2) collect high quality video and range data of dynamic natural scenes under a variety of controlled motion conditions, and 3) test the perceptual implications of these models in psychophysical experiments. The computational models will utilize non-linear decomposition - i.e., the ability to explain complex, time-varying images in terms of the non-linear interaction of multiple factors, such as the interaction between observer motion, the 3-dimensional scene layout, and surface patterns. Importantly, the components of these models will be adapted to the statistics of natural motion patterns that arise from observer motion through natural scenes and movement around points of fixation.The project is a collaboration between three laboratories that have played a leading role in developing theoretical models of natural image statistics, visual neural representations, and perceptual processes. The investigators seek to combine their efforts to develop new models, data sets, and characterizations of 3-dimensional natural scene structure that go beyond previous studies of natural image statistics, and that can be tested in neurophysiological and psychophysical experiments. This project has the potential to bring about fundamental advances in neuroscience, visual perception, and computer vision by developing new classes of models that robustly infer representations of the 3-dimensional natural environment. It will create a set of high quality databases that will be made available to help other investigators study these issues. It will also open up new possibilities for generating realistic stimuli that can guide novel investigations of neural representation and processing.