RI: Small: Recovering Object 3D Shape and Material from Isolated Images

这个项目提高了计算机从视觉传感器解读物体形状和材料的能力。该研究假设，通过将来自观察对象的视觉特征与来自数据集中的已知形状的对象进行匹配、传递已知形状以及对3D形状进行变形以更好地解释匹配特征的空间对应关系，可以估计完整的3D对象形状。这项研究代表了多个尺度的材料，分别编码了材料类别的图案中的小凸起和凹槽。由于图像属性来自形状、材质和照明的组合，因此该研究还涉及开发联合估计的算法。开发的技术可应用于自动化系统、个人和工业机器人、监控和安全、交通、图像检索、图像编辑和处理以及内容创作。该项目通过学生项目、课程开发以及涉及更广泛受众的研讨会和教程来促进教育。这项研究研究了3D形状和材质的改进表示，以及从一幅图像中恢复它们的方法。研究团队没有瞄准真实的模型，例如精确的曲面法线或BRDF参数，而是恢复了对对象识别、内容创建和其他任务有用的近似模型。有关3D对象形状的工作集中于将对象边界标记为遮挡、折叠或纹理/反照率，并将这些边界用作数据驱动方法的一部分，以恢复对象的完整3D模型。这项研究包括研究恢复构成物体的材料的丰富、多尺度表示的方法。这些方法利用照明的近似形状表示和近似表示来恢复对象在某一点的辐射特性的估计。这些算法建立了这些材料属性的地图，以模拟反照率的空间变化和大理石中的纹理等复杂现象。这项研究还涉及扩展这些方法，以报告捕捉树皮等形状纹理的空间变化的法线贴图。最后，研究了如何结合以图像为中心的地图来捕捉更多随机的、空间局部化的现象，如橘子皮上的凹坑。

This project improves a computer's ability to interpret the shape and material of objects from visual sensors. The research hypothesizes that full 3D object shape can be estimated by matching visual features from an observed object to an object of known shape from a dataset, transferring the known shape, and deforming the 3D shape to better account for spatial correspondences of matched features. The research represents materials at multiple scales, separately encoding little bumps and grooves from the patterns of material categories. Because image properties arise from the combination of shape, material, and illumination, the research also involves developing algorithms to jointly estimate. The developed technologies can be applied to automated systems, personal and industrial robotics, surveillance and security, transportation, image retrieval, image editing and manipulation, and content creation. The project contributes to education through student projects, course development, and workshops and tutorials involving a broader audience. The research investigates improved representations of 3D shape and material and methods to recover them from one image. Rather than aiming for veridical models, such as precise surface normals or BRDF parameters, the research team recovers approximate models that are useful for object recognition, content creation, and other tasks. The work on 3D object shape focuses on labeling object boundaries as occlusions, folds, or texture/albedo and using these boundaries as part of a data-driven approach to recover full 3D models of the objects. The research involves studying methods to recover rich, multiscale representations of the materials that compose objects. These methods exploit approximate shape representations and approximate representations of the illumination to recover estimates of radiometric properties of the object at a point. The algorithms build maps of these material properties to model spatial variation in albedo and complex phenomena like veins in marble. The research also involves extending these methods to report spatially varying normal maps that capture shape textures like the bark of trees. Finally, the research investigates how to incorporate image-centered maps to capture more random, spatially localized phenomena like the pits in orange peel.