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.

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，通过将观测物体的视觉特征与数据集中已知形状的物体进行匹配，转移已知形状，并对三维形状进行变形，以更好地考虑匹配特征的空间对应关系，可以估计出完整的三维物体形状。该研究代表了多个尺度的材料，分别从材料类别的模式中编码小凸起和凹槽。由于图像属性由形状、材料和光照的组合而产生，因此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，监视和安全，运输，图像检索，图像编辑和操作以及内容创建。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。该研究探讨了三维形状和材料的改进表示以及从一张图像中恢复它们的方法。该研究团队的目标不是获取精确的表面法线或BRDF参数等垂直模型，而是恢复对对象识别、内容创建和其他任务有用的近似模型。3D物体形状的工作重点是将物体边界标记为遮挡、褶皱或纹理/反照率，并使用这些边界作为数据驱动方法的一部分来恢复物体的完整3D模型。该研究包括研究恢复构成物体的材料的丰富、多尺度表征的方法。这些方法利用近似形状表示和近似照明表示来恢复物体在一点上的辐射特性估计。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复杂现象，如大理石的纹理。该研究还涉及扩展这些方法来报告空间变化的法线贴图，这些法线贴图捕获了树皮等形状纹理。最后，该研究探讨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。