RI-Small: Multi-level Priors for Multi-view Stereo

人类非常善于感知形状，即使在图像提示似乎不足以解决问题的情况下。 例如，在一定的失真下，人们可以从一张照片中推断出场景的结构。 类似地，它们通常可以估计仅部分可见的表面的形状，例如半遮挡的椅子。 这些非凡的能力是由于人类有能力将联合收割机视觉线索与世界上物体和表面形状的先验信息相结合。在计算机视觉中，利用非常低级别线索的现代多视图立体算法现在产生的形状模型被证明几乎与激光扫描仪一样准确，并且在非常不受约束的设置中这样做，例如，使用互联网共享网站上的照片。 然而，这些算法缺乏拼图的另一块-人类视觉系统利用场景形状的先验信息的能力。 在这项工作中，PI专注于建筑场景的特定领域，其中先前的形状概念特别适用。现有的先验知识通常分为两类：低层次的，通常是偏好重建光滑的表面，和高层次的，如基于模型的技术，具有特定的建筑特征的参数化模板。 PI正在探索这些极端之间的一系列先验，显着增加了低级别先验的表达能力，并定义了一组中级先验。 关键思想是考虑建筑表面的差异特性（例如，曲率行为），并利用在这种情况下经常出现的对称性。 PI正在研究一系列的重建问题和应用，从单视图重建到多视图立体，利用先验和先验selection.Finally，PI正在评估这些技术的潜力，重建详细的建筑模型，从街道，空中和内部视图从互联网。 作为这项评估的一部分，他们正在获得地面实况激光扫描和注册图像，为研究界提供基准。这项研究的结果，即，能够从大量图像中自动重建几何模型的工具，将使一系列重要的应用成为可能，包括3D可视化、定位、通信、识别和文化遗产，这些应用远远超出了传统的计算机视觉问题，并可能对广大人口产生广泛的影响。 http://grail.cs.washington.edu/projects/cpc/

Humans are remarkably good at perceiving shape, even in cases where the image cues alone would seem to be insufficient. For example, up to certain distortions, people can infer the structure of a scene from a single photograph. Similarly, they can often estimate the shape of surfaces that are only partially visible, such as a chair that is half-occluded. These remarkable abilities are due to the human capacity to combine visual cues with prior information about the shapes of objects and surfaces in the world.In computer vision, modern multi-view stereo algorithms that exploit very low-level cues now produce shape models that are proving to be nearly as accurate as laser scanners and are doing so in very unconstrained settings, e.g., using photos from Internet sharing sites. However, these algorithms lack the other piece of the puzzle --- the ability of the human visual system to exploit prior information about scene shape. In this work, the PIs focus on the particular domain of architectural scenes for which prior notions of shape are particularly applicable. Existing priors typically fall into two categories: low-level, usually a preference to reconstruct smooth surfaces, and high-level, such as model-based techniques that have parameterized templates for specific architectural features. The PIs are exploring a range of priors between these extremes, significantly increasing the expressiveness of low-level priors and defining a set of mid-level priors. The key ideas are to consider the differential properties of architectural surfaces (e.g., curvature behavior) and to exploit the symmetries that frequently occur in this setting. The PIs are studying a range of reconstruction problems and applications, from single-view reconstruction to multi-view stereo, that exploit priors and prior selection.Finally, the PIs are evaluating the potential of these techniques to reconstruct detailed architectural models from street-level, aerial, and interior views from the Internet. As part of this evaluation, they are obtaining ground truth laser scans and registered imagery to provide a benchmark for the research community.The outcome of this research, i.e., tools that can automatically reconstruct geometric models from large collections of images, will enable a host of important applications, ranging across 3D visualization, localization, communication, recognition, and cultural heritage, that go well beyond traditional computer vision problems and can have broad impacts for the population at large. http://grail.cs.washington.edu/projects/cpc/