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

RI：小：从孤立图像中恢复对象 3D 形状和材质

• 批准号: 1421521
• 负责人: Derek Hoiem
• 金额: $ 47.66万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421521&HistoricalAwards=false
• 关键词: RI; Small; Recovering; Object; 3D

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.

该项目提高了计算机从视觉传感器中解释对象的形状和材料的能力。 该研究假设可以通过将视觉特征从观察到的对象匹配到数据集的已知形状对象，传递已知形状并将3D形状变形以更好地说明匹配特征的空间对应关系，从而估算完整的3D对象形状。 该研究代表了多个尺度的材料，分别编码了材料类别模式的小凸起和凹槽。 由于图像特性是由形状，材料和照明的组合产生的，因此研究还涉及开发算法以共同估计。开发的技术可以应用于自动化系统，个人和工业机器人技术，监视和安全，运输，图像检索，图像编辑和操作以及创建内容。该项目通过学生项目，课程发展以及讲习班以及涉及更广泛受众的教程为教育做出了贡献。该研究调查了3D形状和材料和方法的改进表示，以从一个图像中恢复它们。研究团队恢复了对象识别，内容创建和其他任务有用的近似模型，而不是针对诸如精确的表面正常或BRDF参数之类的垂直模型。 3D对象形状的工作重点是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。 该研究涉及研究恢复组成对象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，以恢复对象的辐射特性的估计值。 算法构建了这些材料特性的地图，以模拟反照率和复杂现象的空间变化，例如大理石中的静脉。 该研究还涉及扩展这些方法，以报告在空间上变化的正常地图，以捕获树皮等形状纹理。 最后，该研究研究了如何合并以图像为中心的地图，以捕获更多随机的，空间局部的现象，例如橙皮中的坑。