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

RI：大型：协作研究：动态自然场景中的 3D 结构和运动

• 批准号: 1111765
• 负责人: Bruno Olshausen
• 金额: $ 68万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111765&HistoricalAwards=false
• 关键词: RI; Large; Collaborative; Research; 3D

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.

视觉系统如何从动态的二维图像中恢复世界的三维结构，例如环境，表面形状或物体运动的布局在我们的眼中？ 这个问题对于计算机和生物学愿景都是至关重要的。 计算机视觉已经开发了各种算法，用于估计场景的特定方面，例如可以建立随时间的对应关系的三维位置，但是为复杂的自然场景和观看条件获得完整而健壮的场景表示仍然是一个挑战。 生物视觉系统已经发展出了令人印象深刻的能力，表明它们对三维世界具有详细且可靠的表现形式，但是对此进行了良好理解的神经表示，到目前为止，神经生理学研究几乎没有洞悉计算过程。 该项目将通过尝试理解三维场景分析核心的通用原则来追求跨学科的方法。特别是，该项目将1）开发出一种新颖的计算模型，这些模型恢复并表示三维场景信息， 2）在各种受控运动条件下收集高质量的视频和动态自然场景的范围数据，3）在心理物理实验中测试这些模型的感知意义。 计算模型将利用非线性分解 - 即，根据多个因素的非线性相互作用来解释复杂的，时间变化的图像的能力，例如观察者运动，三维场景布局和3维场景之间的相互作用表面图案。 重要的是，这些模型的组成部分将适应自然运动模式的统计数据，这些模式是由观察者运动通过自然场景和固定点围绕固定点移动而产生的。该项目是三个实验室之间的合作，这些实验室在开发理论模型中起着领先作用自然图像统计，视觉神经表示和感知过程。 研究人员试图结合他们开发新模型，数据集和三维自然场景结构的特征的努力，这些模型超出了先前对自然图像统计的研究，并且可以在神经生理学和心理生理实验中进行测试。 该项目有可能通过开发新的模型，从而强劲地推断出三维自然环境的表示，从而实现神经科学，视觉感知和计算机视觉的基本进步。 它将创建一组高质量的数据库，可帮助其他研究人员研究这些问题。 它还将为产生现实的刺激开辟新的可能性，从而指导对神经表示和处理的新研究。